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Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
AI-Powered Finance: Forecasting, Planning & Reporting
Transform your finance career with cutting-edge Generative AI skills that top financial institutions are already using. This comprehensive program equips you with practical AI tools to automate financial reporting, optimize portfolio management, and enhance forecasting accuracy. You'll leverage industry-leading platforms like ChatGPT, Microsoft Copilot, Oracle EPM, and QUILL while learning to build custom AI solutions for complex financial challenges. From automated cash flow optimization to AI-driven portfolio analysis, you'll gain job-ready skills that make you indispensable in today's data-driven finance landscape. Perfect for financial analysts, portfolio managers, and finance professionals ready to lead the AI revolution in finance. No advanced programming required—just basic financial knowledge and curiosity about AI's transformative potential in financial decision-making.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Advanced DeepSeek: Fine-Tuning, Optimization, and Deployment
In the rapidly advancing field of AI, fine-tuning, optimizing, and deploying models like DeepSeek are essential for building specialized, scalable systems. This course covers the most advanced techniques in AI model development, focusing on DeepSeek's adaptation for domain-specific applications such as legal reasoning, performance optimization, and deployment strategies. Through in-depth lessons, learners will explore the fine-tuning process for improving model accuracy, optimizing performance, and deploying DeepSeek models in production environments. You will delve into topics like model distillation, cloud-based deployment strategies, and cost management, enabling you to scale AI systems effectively while ensuring performance meets real-world needs. What makes this course stand out is its practical focus on deployment scenarios and optimization strategies that help learners apply their knowledge directly to the challenges they will encounter in professional settings. You'll gain the expertise to make strategic decisions regarding deployment frameworks, hardware, and production operations, making your AI models not only efficient but also sustainable in long-term applications. This course is ideal for AI practitioners, engineers, and data scientists with experience in machine learning or deep learning. It requires familiarity with machine learning concepts and AI deployment practices. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
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COURSE
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Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Advanced Unix System Programming and Performance
The Advanced Unix System Programming and Performance Specialization offers a deep dive into the architecture, administration, and optimization of Unix-based systems. Across four structured courses, learners will master Unix command-line tools, shell scripting, file and process management, interprocess communication (IPC), and performance diagnostics. By combining theory with hands-on command-line practice, this specialization equips aspiring system administrators, developers, and engineers with the tools to analyze, automate, and troubleshoot Unix environments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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COURSE
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Edureka
Agentic AI Engineering
This specialization introduces you to building intelligent agentic AI systems using modern frameworks such as LangChain, LangGraph, and the Model Context Protocol (MCP). It is designed for developers and AI engineers who want to move beyond single-prompt interactions and build dependable, multi-step AI workflows. You’ll start with the foundations of Agentic AI, learning how agents reason, use tools, and manage context. You’ll then apply prompt engineering, context design, and LCEL workflows to build modular pipelines and intelligent agents. As you progress, you’ll design agents with memory, tools, and structured outputs, and build stateful and multi-agent systems capable of handling complex tasks. The specialization concludes with advanced agent architectures, observability, evaluation, and system-level integration. By the end of this specialization, you will be able to: Explain how intelligent agents are built using LangChain and LangGraph Apply tools, memory, and reasoning to design multi-step agent workflows Design stateful and multi-agent systems to solve complex use cases Evaluate and improve agent behavior using observability and feedback techniques This specialization is ideal for developers and AI engineers with basic Python experience who want hands-on skills in modern agent-based AI system design. Join us now and begin your journey to become an Agentic AI expert.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Allen Bradley Micro850 PLC with IIoT
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you will gain comprehensive expertise in programming and integrating the Allen Bradley Micro850 PLC with Industrial Internet of Things (IIoT) technologies. You will start by learning the fundamentals of the Micro850 PLC and its programming environment, CCW (Connected Components Workbench). The specialization then covers key automation concepts, such as ladder logic, bit-level instructions, timers, and counters, through practical exercises and real-world applications. As you progress, you'll explore more advanced programming techniques like structured text (ST), function block diagrams (FBD), and PID control, enabling you to build complex, efficient control systems. You'll also learn to integrate IIoT features, including MQTT communication and MODBUS TCP, for cloud-based monitoring and control. By the end of the specialization, you will be able to Program and troubleshoot Allen Bradley Micro850 PLCs using CCW software, Implement complex automation logic with ladder logic, structured text, and FBD, Integrate IIoT protocols to enable remote monitoring and control of PLC systems, Interface PLC systems with SCADA, mobile devices, and cloud platforms for real-time control.
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University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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Technology & Coding
University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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Technology & Coding
University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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Technology & Coding
University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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Technology & Coding
University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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Technology & Coding
University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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Technology & Coding
University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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Technology & Coding
University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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Technology & Coding
University of Pittsburgh
Applied Bayesian Data Analysis
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
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University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
University of Michigan
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Apply Machine Learning for Predictive Business Analytics
This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
EDUCBA
Artificial Intelligence with Python: Foundations to Projects
This beginner-to-intermediate Specialization takes you from Python setup and numerical computing to building, tuning, and explaining machine learning and deep learning models. Across three courses, you’ll master data wrangling with NumPy, visualization with Matplotlib and Seaborn, model evaluation and feature engineering, clustering and classification, and NLP workflows using NLTK. The curriculum is project-based and aligned with industry workflows so you graduate with portfolio-ready artifacts that showcase applied AI skills.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
LearnQuest
Blockchain Solution Architecture
This specialization is intended for individuals with a basic understanding of digital technologies as well as developers or security professionals with at least 2 years of programming experience wishing to expand their blockchain knowledge. Throughout the specialization, students will learn about the fundamentals of blockchain architecture, progress to some of the intermediate concepts of blockchain technology, and finish with advanced blockchain architectures.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Illinois Tech
Building with Code: Programming Fundamentals and Open Source
This specialization introduces learners to programming fundamentals, web development, and open-source technologies. Through hands-on projects, learners will build web applications using HTML, CSS, JavaScript, and Python, while exploring open-source frameworks and tools.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Macquarie University
Cyber Security: Essentials for Forensics
This Specialization brings together three industry-relevant courses Digital Forensics, Mobile Security, and AI for Cyber Security to provide you with a complete foundation in modern cyber investigations. You’ll explore the full lifecycle of forensic practice: from retrieving and preserving digital evidence to analysing complex data sets, reporting findings, and understanding the legal frameworks that govern admissibility in court. You’ll gain the skills to investigate cybercrime across networks, and mobile devices, learning how to extract and interpret artefacts from iOS and Android systems, identify hidden data, and uncover evidence of fraud, harassment, or malicious activity. The program also equips you with cutting-edge expertise in artificial intelligence, giving you hands-on experience applying machine learning models to malware detection, network anomaly analysis, and adversarial defence. This forward looking skillset prepares you to lead investigations in an era where AI is reshaping both attacks and defences. Developed by Macquarie University’s Cyber Skills Academy, a top 1% of university globally and recognised as Australia’s leading cyber security school. Every course is co-designed with experts and tailored to the realities of the cyber workforce. By the end, you’ll have the confidence and practical skills to contribute to forensic investigations, strengthen organisational resilience, and build a career at the frontlines of digital defence.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Pearson
Data Science Fundamentals, Part 1
Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
Packt
Data Structures Algorithms in Java – SECRETS to Ace LeetCode
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization will equip you with the skills to master data structures and algorithms in Java, essential for acing coding challenges like those on LeetCode. You will learn to design efficient algorithms, solve complex problems, and understand core principles behind data structures like arrays, linked lists, trees, and stacks. The course begins with an introduction to Java programming, covering input/output operations and debugging. You'll then explore core data structures, including arrays, ArrayLists, and multidimensional arrays. The course also covers advanced topics such as bitwise operations, recursion, and sorting algorithms, followed by more complex structures like linked lists, binary trees, and binary search trees. LeetCode problems will reinforce these concepts throughout. This specialization is ideal for intermediate learners with basic programming knowledge, especially those preparing for technical interviews or aiming to strengthen their understanding of algorithms. By the end, you’ll be able to solve coding problems efficiently, optimize algorithms, and confidently approach technical challenges.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
University of Colorado Boulder
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Packt
Deep Learning with Real-World Projects
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive deep learning course offers a practical journey from the basics to advanced concepts. It starts with perceptrons and neural networks, progressing through key topics such as backpropagation, convolutional neural networks (CNNs), and transfer learning. You'll gain hands-on experience with tools like TensorFlow and Keras, applying deep learning techniques to real-world applications such as medical image analysis and natural image classification. The course ensures you learn not only the theory but also how to build, train, optimize, and deploy neural networks. By the end, you'll have a robust portfolio of projects, showcasing your deep learning skills. Perfect for data scientists and ML engineers, this course requires a basic understanding of Python, mathematics, and ML algorithms. Whether you're advancing your AI career or starting your journey in data science, this course equips you with essential knowledge and practical expertise in deep learning.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
Imperial College London
Digital Health
This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services. The aim of this specialisation is to prepare learners for the new era of digitalisation in public health and health care globally. The design and implementation of digital health interventions aspect of this specialisation address topics to include design thinking, regulatory approaches, ethical considerations, technology adoption, implementation and strategy as applied to digital health. The evaluation component of this course focuses on data considerations in digital health, data management and the evaluation of digital health interventions with a focus on experimental and quasi-experimental design approaches to evaluation and the economic evaluation of digital health interventions.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
University of Illinois Urbana-Champaign
Digital Marketing: Audience, Campaigns, and Metrics
In this Specialization, you’ll learn how to plan, execute, and measure digital marketing campaigns in a connected, data-driven world. You’ll treat marketing as strategic communication—choosing channels that match your audience, goals, and message timing across both traditional and digital media. You’ll also explore how emerging technologies like AI, the Internet of Things, and mixed reality are changing how brands reach and engage customers. You’ll then build an integrated campaign workflow: create a digital marketing communication plan, mix paid, earned, owned, and shared media, and evaluate the role of social platforms in viral and influencer campaigns. You’ll learn to measure performance by selecting KPIs, interpreting results, and analyzing ROI—while identifying and managing common risks in digital marketing. Finally, you’ll strengthen customer engagement and content execution. You’ll design content for environments where humans, algorithms, and generative AI all respond differently, and you’ll learn how paid, organic, and influencer efforts work together as an ecosystem. You’ll also develop practical content planning and management skills, including audience-based planning, content audits, and content governance.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
The Hong Kong University of Science and Technology
Discrete Mathematical Tools for Computer Science
This specialization builds the core discrete mathematics toolkit used throughout computer science, with a focus on logic, counting, algorithms, recursion, and probability. Learners develop rigorous problem-solving and reasoning skills that are essential for algorithm analysis, data structures, cryptography, and theoretical foundations of computing. Through practical examples and proofs, the courses emphasize how discrete mathematical concepts directly support efficient and correct algorithm design.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
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Technology & Coding
Packt
Embedded Systems Object-Oriented Programming in C and C++
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will embark on a journey through the fundamentals and advanced techniques of embedded systems programming with a focus on object-oriented methodologies. Beginning with the setup of essential tools like Keil uVision and STM32CubeIDE, you will gain hands-on experience in configuring and using these powerful development environments. As you progress, the course delves into object-oriented firmware programming, starting with the creation of drivers and classes in both C and C++. You will learn to develop UART drivers, create LED classes, and implement inheritance, all while adhering to best practices. The course also explores the development of GPIO and UART libraries from scratch, providing a robust understanding of how to build and test reusable code components in an embedded context. Finally, the course covers advanced topics such as polymorphism and the extension of GPIO libraries to handle alternate functions. By the end of this course, you will have a comprehensive understanding of object-oriented programming in embedded systems, enabling you to create efficient, scalable, and maintainable firmware for a wide range of applications. This course is designed for embedded systems engineers, firmware developers, and hobbyists with a basic understanding of C programming. Familiarity with microcontrollers and basic electronics is recommended but not required.
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Whizlabs
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Technology & Coding
Whizlabs
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Whizlabs
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Whizlabs
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Whizlabs
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Whizlabs
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Whizlabs
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Whizlabs
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808]
Exam Prep: Oracle Certified Associate, Java SE 8 [1Z0-808] specialization is designed for developers preparing to validate their core Java programming skills and understanding of object-oriented principles. This course systematically builds your foundation from basic syntax to advanced programming constructs required for the certification. The program deepens expertise in working with arrays, loops, and decision constructs, and progresses to essential OOP concepts such as methods, encapsulation, inheritance, and polymorphism. By completion, learners will be fully prepared to demonstrate their ability to develop and debug robust Java applications, an essential credential for aspiring Java developers, software engineers, and backend programmers pursuing Oracle’s globally recognized Java SE 8 certification. You’ll gain practical experience in: Define variables, construct classes, and create executable applications Implement operators, loops, and conditional statements Use control flow and looping constructs Apply OOP concepts (inheritance, polymorphism, and interfaces) By the end of this training, you'll be ready to confidently pass the Oracle Certified Professional Java SE 8 Programmer I (1Z0-808) exam, and more importantly, step into entry-level roles that require foundational Java development skills and a solid understanding of object-oriented programming.
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Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Technology & Coding
Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Technology & Coding
Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Technology & Coding
Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Technology & Coding
Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Technology & Coding
Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Technology & Coding
Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Technology & Coding
Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Technology & Coding
Coursera
Facebook Content Creator Pro
Transform from Facebook beginner to marketing expert with this 20-course program covering every aspect of platform success. Starting with zero experience, you'll build a thriving presence through strategic planning, compelling storytelling, and data-driven optimization. Master content strategy by identifying your niche and creating balanced calendars mixing posts, visuals, videos, and live content. Learn to craft posts using the Hook-Body-CTA model, create visuals with mobile photography, and produce videos for Facebook Watch. Master Facebook Live for real-time connection and understand the algorithm to maximize reach. Use Meta Business Suite analytics while building communities through Groups and Pages. The program includes business training—from page setup to Facebook Ads Manager mastery. Learn monetization through Stars, Subscriptions, Events, and Shops. Special courses cover AI content with ChatGPT, Canva animation, and organic Group marketing. Through hands-on projects and templates, gain immediately applicable skills. Whether building a personal brand, growing a business, or pursuing content creation, this program provides the complete roadmap from first post to profits. By completion, you'll confidently create content, build communities, run ad campaigns, and generate multiple revenue streams on Facebook.
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Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Università di Napoli Federico II
Finance
This Finance specialization is intended for students who want to acquire the analytical and empirical tools needed to understand the functioning of financial markets. Students will learn how investors choose their portfolios and how their choices determine equilibrium asset prices. Students will analyze the role of liquidity in securities markets, and they will understand how security trading is organized and regulated and how it has been reshaped by algorithmic and high frequency trading, and how the trading process affects the formation of asset prices. The program will equip students with the tools and the skills necessary to pursue a career in the financial industry.
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Technology & Coding
Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
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Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
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Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
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Technology & Coding
Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Foundations of AI in Web Development
This course introduces the core principles and strategies of artificial intelligence (AI) in web development, showcasing how AI is transforming modern web experiences. From enhancing user interactions to automating complex processes, AI is at the forefront of shaping the web's future. You will learn how to harness AI technologies such as machine learning, natural language processing, and computer vision to develop intelligent applications. The course will also help you understand AI's potential to streamline workflows and improve overall web design. What sets this course apart is its practical approach, offering real-world examples and case studies that highlight AI’s application in web projects. You will gain both theoretical knowledge and hands-on skills to effectively integrate AI into your web applications. Designed for web developers looking to understand AI’s capabilities, this course requires a basic understanding of web development but is open to anyone eager to explore the world of AI-driven web solutions. This course is part one of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations of Data Science
This is the first course in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the eight courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the functions of data analytics and data science within an organization -Identify tools used by data professionals -Explore the value of data-based roles in organizations -Investigate career opportunities for a data professional -Explain a data project workflow -Develop effective communication skills
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
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Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Google
Foundations: Data, Data, Everywhere
This is the first course in the Google Data Analytics Certificate. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks using the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Gain an understanding of the practices and processes employed by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices you can leverage during your job search.
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Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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COURSE
FREE TRIAL
Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Rice University
Fundamentals of Computing
This Specialization covers much of the material that first-year Computer Science students take at Rice University, brought to you by the world-class Faculty who teach our master's and PhD programs. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Packt
Game Math Foundations - Unity 6 Compatible
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. This specialization delves into key game math concepts for Unity 6, focusing on bitwise operations, vector math, intersections, and affine transformations. Learners will build a solid foundation in these areas to solve complex game development challenges. It starts with bitwise operations and progresses to advanced topics like vectors, rotations, and transformations. Practical exercises help learners master concepts such as Cartesian coordinates, dot products, and cross products in Unity. As you advance, you'll apply mathematical principles in Unity 6, following step-by-step solutions to challenges that help you implement what you've learned in real-world game development scenarios. This specialization is ideal for game developers, aspiring Unity users, and anyone interested in learning how to use mathematics in game development. It is recommended for learners with a basic understanding of programming and game development. By the end, you will be able to apply fundamental mathematical concepts to solve real-world problems in Unity, implement advanced vector and transformation techniques, and develop optimized solutions for game mechanics and physics.
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
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Technology & Coding
Google Cloud
Geliştiriciler İçin Sorumlu Yapay Zeka
This specialization equips developers with the essential knowledge and skills to build responsible AI systems by applying best practices of Fairness, Interpretability, Transparency, Privacy, and Safety. Throughout the courses, you will learn how to: Identify and Mitigate Bias: Learn to recognize and address potential biases in your machine learning models to mitigate fairness issues. Apply Interpretability Techniques: Gain practical techniques to interpret complex AI models and explain their predictions using Google Cloud and open source tools. Prioritize Privacy and Security: Implement privacy-enhancing technologies like differential privacy and federated learning to protect sensitive data and build trust. Ensure Generative AI Safety: Understand and apply safety measures to mitigate risks associated with generative AI models. By the end of this specialization, you will have a comprehensive understanding of responsible AI principles and the practical skills to build AI systems that are ethical, reliable, and beneficial to users.
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Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
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Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
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COURSE
FREE TRIAL
Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
Generative AI Engineering with LLMs
The Gen AI market is expected to grow 46% . yearly till 2030 (Source: Statista). Gen AI engineers are high in demand. This program gives aspiring data scientists, machine learning engineers, and AI developers essential skills in Gen AI, large language models (LLMs), and natural language processing (NLP) employers need. Gen AI engineers design systems that understand human language. They use LLMs and machine learning to build these systems. During this program, you will develop skills to build apps using frameworks and pre-trained foundation models such as BERT, GPT, and LLaMA. You’ll use the Hugging Face transformers library, PyTorch deep learning library, RAG and LangChain framework to develop and deploy LLM NLP-based apps. Plus, you’ll explore tokenization, data loaders, language and embedding models, transformer techniques, attention mechanisms, and prompt engineering. Through the series of short-courses in this specialization, you’ll also gain practical experience through hands-on labs and a project, which is great for interviews. This program is ideal for gaining job-ready skills that GenAI engineers, machine learning engineers, data scientists and AI developers require. Note, you need a working knowledge of Python, machine learning, and neural networks.. Exposure to PyTorch is helpful.
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Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
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Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
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COURSE
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Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Harnessing LLMs: Strategy, Fine-Tuning & Evaluation
Large Language Models are revolutionizing how businesses operate, from customer support to content generation. This comprehensive program takes you from LLM business strategy to production deployment, combining strategic thinking with hands-on technical skills. You'll learn to evaluate LLM opportunities, fine-tune models for specific tasks, and build production-ready applications using industry-standard tools like Hugging Face, Python, and cloud platforms. The program covers essential topics including business implementation strategies, model evaluation techniques, fine-tuning approaches, and ethical AI deployment. Whether you're a business leader seeking AI strategy insights or a technical professional building LLM applications, you'll gain practical skills to leverage these transformative technologies. By completion, you'll understand how to select appropriate models, implement custom solutions, and deploy responsible AI systems that drive real business value across industries.
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Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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COURSE
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Technology & Coding
University of Pennsylvania
How to Use Data
“How to Use Data” is designed to equip learners with the essential skills needed for a career in data analytics. This specialization emphasizes the ability to scope and answer critical business questions using data while providing a comprehensive foundation in key data analytics processes. In the first course, you’ll explore the fundamentals of data analysis, data science, and data analytics, learning about essential tools and programming languages through real-world case studies. You will master techniques like data wrangling with SQL, gaining hands-on experience with data storage, access, and manipulation using relational databases. Moving into exploratory data analysis (EDA) with Python, you’ll develop skills in data inspection, querying, summarization, and visualization. Additionally, you’ll learn how to apply predictive analytics techniques—such as regression, decision trees, random forests, and clustering—to solve complex business challenges and make data-driven predictions. Finally, you’ll gain expertise in creating impactful visualizations with Tableau and presenting data insights effectively to stakeholders, enabling you to drive informed decision-making in real-world scenarios.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
IBM & IBM
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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COURSE
FREE TRIAL
Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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COURSE
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Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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COURSE
FREE TRIAL
Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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Technology & Coding
Northeastern University
Information Systems Foundations
The Information Systems Foundations specialization provides an immersive experience into software development and information systems across four courses. Designed to progressively build from basic concepts to advanced applications, it aims to arm you with practical skills in software engineering, domain modeling, and Python programming. Beginning with advanced software engineering techniques and systems thinking, this specialization advances through object-oriented programming, culminating in hands-on Python projects that tackle real-world problems. Throughout this specialization, you'll master the art of modeling significant business applications swiftly and effectively, preparing you for challenges in the professional world of information systems.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Business Analytics and Information Economics
This specialization targets learners who seek to understand the opportunities that data and analytics present for their organization and those interested in the value of and implications for data as an asset to their organization. Individuals who manage data and make decisions about how data can be leveraged in their organization will find this specialization of particular value. Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. In this information age, the value of data as a business asset is essential. Organizations must creatively consider and implement new ways to generate economic benefits from the wide array of information assets available. Unfortunately, information frequently is under-appreciated and underutilized. Besides, accounting practices fail to recognize the financial value of information, and traditional asset management practices fail to recognize information as an asset to be managed with earnest discipline. This has led to a business culture of complacence, and the inability for organizations to leverage available information assets.
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Technology & Coding
SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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Technology & Coding
SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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Technology & Coding
SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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Technology & Coding
SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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Technology & Coding
SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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Technology & Coding
SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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Technology & Coding
SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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Technology & Coding
SkillUp
Introduction to Healthcare Data Analytics
Healthcare organizations generate vast and complex data across clinical, operational, and financial systems. This specialization is designed to equip you with the end-to-end analytics and visualization skills needed to work confidently with healthcare data and turn it into meaningful, decision-ready insights. The specialization includes three short courses, eachrequiringapproximately8-9hours of learner engagement. Across the three hands-on courses, you will learn how to identify, prepare, and analyze healthcare data from diverse sources, apply statistical and predictive modeling techniques, and design executive-ready dashboards that support clinical, operational, and strategic decision-making. You will work with real-world healthcare datasets using industry-relevant tools such as Python, Excel, SQL, and Google Looker Studio, while also developing a strong understanding of data privacy, ethics, and regulatory requirements. By moving from foundational data understanding to applied analytics and executive communication, this specialization emphasizes practical, job-ready skills. You will gain experience analyzing healthcare performance, evaluating clinical outcomes, and communicating insights clearly to clinicians, administrators, and leadership—preparing you for roles such as healthcare data analyst, clinical analyst, health informatics specialists, or healthcare business intelligence professional.
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Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Technology & Coding
Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Technology & Coding
Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Technology & Coding
Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Technology & Coding
Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Technology & Coding
Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Technology & Coding
Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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Technology & Coding
Board Infinity
Java in Machine Learning
This Specialization equips learners with the skills to design, implement, and deploy machine learning solutions using Java. Starting with core ML concepts like regression, classification, and clustering, learners will apply Java-based tools such as Weka, Smile, Tribuo, and Deeplearning4j to build real-world models. The courses cover data preprocessing, model training, evaluation, deep learning, NLP, and large-scale ML with Spark and Mahout. Learners will also explore advanced topics like federated learning and MLOps practices using Jenkins and GitHub Actions. By the end of the specialization, participants will be able to create and deploy scalable ML applications in enterprise environments with Java. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.
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IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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Technology & Coding
IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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Technology & Coding
IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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Technology & Coding
IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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Technology & Coding
IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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IBM
JavaScript Programming with React, Node & MongoDB
If you want to learn a single language used for both front-end and back-end development, this JavaScript specialization from IBM is for you. You can use this versatile, popular programming language to architect cloud-based, interactive applications. Industry leaders choose JavaScript’s popular React library for crafting dynamic user experiences and creating modular, server-side applications using the Node.js Express framework. This specialization contains courses on each of these robust JavaScript technologies and more! In the final course, you will learn to connect your JavaScript applications to the open-source NoSQL database, MongoDB. You’ll want to learn about NoSQL databases because contemporary applications require their flexibility for querying large amounts of unstructured data. Finally, you will write REST APIs to get all of these services to communicate with each other. Throughout this program, you will develop several applications with these various technologies. Upon completing the full program, you will have a portfolio of JavaScript projects to provide you with the confidence to excel in your interviews. We highly recommend you have a familiarity with HTML and CSS concepts and their syntax. A working knowledge of a version control system such as Git or GitHub is recommended but not essential.
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Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
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Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
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Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
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Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
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Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Johns Hopkins University
Large-Scale Database Systems
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Packt
Learn to Code with Ruby
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Start your programming "Learn to Code with Ruby" is an educational journey designed to transform beginners into proficient programmers. Starting with installation and configuration on various operating systems, the course covers Ruby’s object-oriented principles, variables, data types, and control structures. Practical exercises simulate real-world coding environments, emphasizing problem-solving and critical thinking. The course highlights the importance of programming in today's tech-driven world and lays a solid foundation for web development with Ruby on Rails. Learners will gain skills in: - String, number, and Boolean manipulation, - Collections like arrays and hashes, - Advanced topics like blocks, procs, lambdas, - Object-oriented programming with classes and modules. By the end, students will be equipped to handle real-world programming challenges confidently. Ideal for novice programmers, intermediate coders, and web developers, the course promises a deep understanding of Ruby and its applications.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
Vanderbilt University
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
University of Washington
Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud & New York Institute of Finance
Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.
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Technology & Coding
Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Technology & Coding
Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Technology & Coding
Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Technology & Coding
Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Technology & Coding
Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Technology & Coding
Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Technology & Coding
Google Cloud
Machine Learning with TensorFlow on Google Cloud em Português Brasileiro
O que é aprendizado de máquina e que tipos de problema ele pode resolver? Quais são as cinco fases da conversão de um possível caso de uso de aprendizado de máquina e por que é importante que elas não sejam ignoradas? Por que as redes neurais são tão requisitadas hoje? Como configurar um problema de aprendizado supervisionado, além de encontrar uma solução ótima e generalizável com gradiente descendente e uma boa forma de criar conjuntos de dados? Aprenda a gravar modelos de aprendizado de máquina distribuídos com escalonamento no TensorFlow, faça escalonamento horizontal do treinamento desses modelos e ofereça previsões de alto desempenho. Converta dados brutos em atributos para informar características importantes desses dados ao aprendizado de máquina e ofereça uma percepção humana para dar suporte ao problema. Por fim, aprenda a incorporar a combinação ideal de parâmetros que produz modelos precisos e generalizados, além de conhecer a teoria para resolver tipos específicos de problemas de aprendizado de máquina. Você passará por todas as etapas do aprendizado de máquina, desde a criação de uma estratégia voltada para aprendizado de máquina até o treinamento, a otimização e a produção de modelos em laboratórios práticos com o Google Cloud Platform. >>> Ao se inscrever nesta especialização você concorda com os Termos de Serviço do Qwiklabs conforme estabelecido na seção de perguntas frequentes. Veja os Termos de Serviço aqui: https://qwiklabs.com/terms_of_service <<<
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Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
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Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
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Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
Master Dialogflow CX Agents
Conversational AI has transformed customer engagement, with 45% of support queries now resolved automatically by advanced agents. This Short Course was created to help Software Development professionals accomplish rapid deployment of intelligent virtual assistants using Google's enterprise-grade Dialogflow CX platform. By completing this course, you'll be able to design intent structures that achieve 85%+ match accuracy, diagnose mis-routed utterances through transcript analysis, calculate critical NLU performance KPIs, and build webhook integrations that retrieve live data in under 1 second—capabilities you can deploy to staging tomorrow. By the end of this course, you will be able to: ● Apply intent-classification heuristics to build five new intents that achieve ≥85% training-phrase match accuracy in Dialogflow CX ● Analyze one week of chat logs, isolate three mis-routed utterances, and correct them by refining entities or training phrases ● Evaluate agent quality by exporting fulfillment diagnostics, calculating NLU accuracy, latency, and human-handoff rate, and recommending two optimization actions ● Create a secure webhook (Node.js or Python) that calls an external REST API and returns dynamic data to the user in ≤1 second round-trip (Create) This course is unique because it combines hands-on Dialogflow CX development with diagnostic methodologies for measuring and improving conversational agent performance, bridging the gap between building chatbots and deploying enterprise-grade AI systems that meet production SLAs. To be successful in this project, you should have a background in API integration, basic Python or Node.js programming, and software development practices at CB2 intermediate-level expertise.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Technology & Coding
EDUCBA
Master Java Spring Framework: Build Web Apps
This specialization provides a complete, hands-on journey into the Java Spring Framework—covering Inversion of Control (IoC), dependency injection, aspect-oriented programming (AOP), and the MVC architecture. Learners progress from foundational Java concepts to developing full-fledged enterprise-level web applications. Through practical, project-based learning, participants design relational databases, configure Spring components, and build complete shopping cart systems using Spring MVC and Hibernate. By the end, learners will have both theoretical mastery and practical expertise to build, deploy, and maintain scalable web solutions using modern Spring practices.
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Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
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Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
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Technology & Coding
Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
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Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
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Technology & Coding
Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Edureka
Mastering Power BI for Data Analytics & Storytelling
This Power BI specialization is suitable for individuals who are new to the field, as well as business analysts, database administrators, BI developers, IT professionals, data analysts, data engineers, and data scientists. This program is designed to help you enhance your abilities in designing databases, managing and transforming data, utilizing DAX, creating reports, publishing dashboards, generating and observing AI insights, applying machine learning, building analytic applications, analyzing AI-generated reports, and managing data sources, all through our carefully curated 5-course structure. You will learn to preprocess and analyze data, create insightful reports and dashboards, and perform AI analytics using image and text data. This Power BI specialization equips you with the necessary skill set for efficient data management and powerful visual storytelling.
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Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
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Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
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FREE TRIAL
Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
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COURSE
FREE TRIAL
Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
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FREE TRIAL
Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Real Madrid Graduate School Universidad Europea
Maximum Performance and Technology in Sports
This specialization explores the technologies transforming modern football, from advanced data analysis and machine learning to AI-powered scouting and next-generation performance systems. Across four applied courses, learners will trace the evolution of football analytics, build essential statistical and technical foundations, examine real-world tactical and scouting case studies, and discover the emerging innovations that will shape how teams train, recruit, and compete in the future. The curriculum integrates event data, tracking data, live data, and machine-learning techniques with cutting-edge tools such as Large Language Models, computer vision, web scraping, contextual load monitoring, and automated tactical recognition. Through examples from elite football and pioneering research, learners will understand how AI is reshaping decision-making across performance analysis, talent ID, recruitment, coaching, strategy, and club operations. Developed for analysts, coaches, scouts, and professionals seeking to future-proof their skills, the specialization emphasizes practical implementation. Across each module, learners apply concepts to match situations, evaluate data workflows, and experiment with emerging technologies. By completing the program, they will gain a forward-looking, industry-ready understanding of how technology, automation, and intelligent systems will redefine performance and competitive advantage in football.
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Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
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FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Meta
Meta Web Development Fundamentals
Whether a complete beginner or looking to enhance your existing skills, the Meta Web Developer Fundamentals program is designed to equip aspiring developers with essential skills to enter the field of software and web development. Starting with the basics, the program will cover the core technologies that power the internet. Learn how to create attractive and responsive web pages using HTML and CSS, and harness the power of modern UI frameworks like Bootstrap. As the program progresses, it will teach the intricacies of front-end development as well as how to create user-friendly interfaces that work seamlessly across various devices. This program goes beyond writing html and css. It will also explain the fundamentals of Python programming, database management, and how to build robust web applications using the Django framework. Gain hands-on experience with SQL, understand the principles of data storage and retrieval, and learn how to design and implement efficient database systems. Throughout the program, learners will also develop crucial skills in version control using Git, collaborate effectively with other developers, and learn best practices in software development workflows. By the end of this program, learners will have a well-rounded skill set covering both front-end and back-end technologies, positioning you for success in the competitive world of web development.
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Technology & Coding
Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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Technology & Coding
Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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Technology & Coding
Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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Technology & Coding
Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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Technology & Coding
Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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Technology & Coding
Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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Technology & Coding
Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Microsoft
Microsoft Advanced Analytics Techniques with Generative AI
Microsoft Advanced Analytics Techniques with Generative AI helps you build modern analytics skills by combining proven data methods with emerging AI tools. You’ll strengthen your ability to analyze structured and unstructured data, automate workflows, create forecasts, and support strategic decisions using practical techniques employers value. Across five courses, you’ll work with R, GitHub Copilot, Power BI, Excel, Azure Blob Storage, APIs, and reporting tools to solve realistic business problems. You’ll move from statistical testing and regression analysis to time-series forecasting, scenario planning, anomaly detection, and automated coding workflows. You’ll learn how to improve efficiency with generative AI while maintaining analytical judgment. The program emphasizes using AI responsibly to generate code, streamline documentation, accelerate data cleaning, and uncover insights faster. By the end of the specialization, you’ll be able to build predictive models, automate repetitive tasks, create dynamic reports, evaluate risks, and communicate findings clearly to stakeholders. This specialization is designed for analysts, technical professionals, and experienced learners who want to modernize their skills with AI-enhanced analytics. Prior experience with data analysis, spreadsheets, or basic programming is recommended.
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LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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Technology & Coding
LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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Technology & Coding
LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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FREE TRIAL
Technology & Coding
LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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FREE TRIAL
Technology & Coding
LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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Technology & Coding
LearnQuest
Microsoft Azure: AI, Infrastructure, and Data Solutions
This four-course Specialization provides a practical, end-to-end introduction to Microsoft Azure, spanning core infrastructure, data analytics, and applied artificial intelligence. Learners progress from configuring secure Azure environments and virtual networks, to processing and analyzing data with modern analytics tools, and finally to building, training, and deploying machine learning models using Azure Machine Learning and Cognitive Services. The curriculum emphasizes hands-on implementation and standardized industry practices, including Microsoft’s Team Data Science Process, preparing learners to make informed technical decisions and deliver resilient, scalable cloud solutions aligned to real organizational needs.
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EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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Technology & Coding
EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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Technology & Coding
EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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Technology & Coding
EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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Technology & Coding
EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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Technology & Coding
EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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Technology & Coding
EDUCBA
Microsoft Azure: Cloud Solutions Mastery
This Specialization delivers a comprehensive path for mastering Microsoft Azure across core domains—AI, data engineering, cloud architecture, application development, and platform migration. Learners will gain hands-on experience with tools such as Azure Machine Learning, Cognitive Services, App Services, Data Factory, and PaaS environments. With skill-building modules aligned to Microsoft certifications (e.g., DP-100, DP-300, DP-900, AI-900), this program prepares professionals to design intelligent, scalable, and secure solutions across diverse cloud scenarios.
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Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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COURSE
FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Microsoft
Microsoft Secure & Scalable API Development with .NET
Build practical back-end development skills with .NET and learn how to create secure, scalable, and high-performing APIs for modern applications. This Professional Certificate helps you understand the complete API development lifecycle—from building APIs with ASP.NET Core to implementing authentication, improving performance, and scaling applications for real-world use cases. You’ll start by learning .NET architecture, C# fundamentals, API routing, dependency injection, middleware, serialization, and OpenAPI integration. As you progress, you’ll develop skills in authentication and authorization using ASP.NET Identity, JSON Web Tokens (JWT), role-based access control, encryption, and secure data handling practices. The program also introduces performance optimization concepts including caching strategies, database query optimization, distributed systems, load balancing, and scalable application architecture. Throughout the certificate, you’ll use Microsoft Copilot to accelerate coding, debugging, optimization, and secure development workflows. By the end of the program, you’ll be able to develop and secure APIs, optimize application performance, implement scalable architectures, and apply modern development practices used by back-end engineers. This program is designed for aspiring back-end developers, software engineers, and learners interested in API development and application security. No prior programming experience is required.
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Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
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Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
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Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
EDUCBA
Modern Financial Markets & Risk Management
Master modern financial markets, fintech, and risk management for FRM II success. Learn how real-world financial systems evolve with technology, liquidity, and global risks. This course provides a structured and practical understanding of current issues in financial markets, focusing on fintech, blockchain, big data, liquidity risk, repo markets, and global banking dynamics. Designed for FRM Part II candidates and finance professionals, it bridges the gap between theoretical concepts and real-world applications. You will explore how digital transformation is reshaping financial services, analyze liquidity crises and funding markets, and understand the impact of global USD shortages. The course also covers machine learning applications in finance, exchange rate mechanisms, interest rate parity, and asset-liability management strategies. By the end of this course, learners will be able to confidently interpret complex financial systems, evaluate risk exposures, and apply advanced financial concepts in both exam and professional settings.
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![Modern JavaScript from The Beginning [Second Edition]](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://d15cw65ipctsrr.cloudfront.net/35/db2553989d4b8fa2872fd9ac6478f9/skill_image_square.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop)
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Modern JavaScript from The Beginning [Second Edition]
Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will start by exploring the basics of JavaScript, from variables, data types, and methods, to advanced concepts like asynchronous programming and Object-Oriented Programming (OOP). You will learn how to structure JavaScript projects, utilize modern tooling like Webpack, and work with APIs to create real-world applications. By the end of this course, you will have a deep understanding of JavaScript, ready to develop web apps with dynamic and interactive features. Through hands-on projects, you will apply what you’ve learned to solve practical problems like creating a shopping list app, a movie application, and a personalized tracker. These projects will reinforce your knowledge and allow you to build a robust portfolio of real-world JavaScript applications. This course is designed for beginners, with no prior experience required, although familiarity with basic programming concepts will help. As you progress, you’ll gradually tackle more advanced topics, making this a comprehensive introduction to modern JavaScript development.
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Rice University
Parallel, Concurrent, and Distributed Programming in Java
Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers. To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).
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Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
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Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
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Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Maryland, College Park
Platform Product Management
During this course the student will go through a full Low Code/No Code platform lifecycle. Learners will get an understanding of how to lead Citizen Developers (CD) and learn how to deploy solutions that will deliver success. This 3-course program has modules that will guide students through the workflow and processes of building an enterprise solution in a Low Code/No Code environment with the aid of templates. By the end, students will have done some low-code app development without the need of prior coding experience or programming language. The end result being the following deliverables: An analysis of the development environment, business users and the solution where they want to build an enterprise application. A strategic plan to deliver and build a visual model of their desired solution. A fully functional application on the platform of their choosing. (An example being a new mobile app). An enterprise-grade change management solution and road map for the future of their custom application. On top of learning new applications, we want to provide a deeper understanding of the benefits of low-code vs no-code development, low-code tools, low-code application platforms, low-code application development, and the user interface. As part of this certification class, we will review the following Low Code/No Code platforms: Service Now Appian OutSystems Mendix Salesforce Zoho App Sheets This is just a sampling of platforms we will cover.
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Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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FREE TRIAL
Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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FREE TRIAL
Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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FREE TRIAL
Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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FREE TRIAL
Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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COURSE
FREE TRIAL
Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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COURSE
FREE TRIAL
Technology & Coding
Dartmouth College
Practical Machine Learning: Foundations to Neural Networks
You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.
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Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
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Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Duke University
Programming for Python Data Science: Principles to Practice
Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline. Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems. In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.
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Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
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Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
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FREE TRIAL
Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
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Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
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Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
EDUCBA
Python Hacking & Cryptography Mastery
The Python Hacking & Cryptography Mastery Specialization takes learners from beginner to advanced levels in both Python programming and cybersecurity fundamentals. Through a structured series of practical, hands-on courses, learners will explore classical encryption, transposition, and substitution ciphers while mastering Python’s role in cryptanalysis. Each course combines coding, mathematics, and ethical hacking to provide deep technical insight and problem-solving skills. By the end of the specialization, learners will be equipped to design, analyze, and break cryptographic systems using Python—preparing them for cybersecurity, software, or ethical hacking careers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
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Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
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Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Python for DevOps: The Ultimate Hands-On Guide
This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. The Python for DevOps specialization provides hands-on experience in automating DevOps tasks using Python. You'll gain key skills, from setting up the Python environment to mastering core Python concepts, system interactions, and CI/CD pipeline creation. The course covers Python’s role in automation, scripting, logging, and error handling, preparing you to apply Python in real-world DevOps workflows. You’ll start with environment setup, installation, and version management using pyenv. The course then covers core Python concepts like data structures, loops, functions, object-oriented programming, and advanced topics such as decorators and generators. Practical examples help reinforce learning, enabling you to tackle DevOps challenges confidently. This intermediate-level course is ideal for DevOps engineers, software developers, and IT professionals with prior programming experience. Familiarity with DevOps principles is recommended. By the end of the specialization, you will be able to: Develop Python-based automation scripts for DevOps tasks. Set up and manage Python environments for DevOps projects. Automate system interactions and error handling. Build, test, and deploy Python applications within CI/CD pipelines.
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Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
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Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
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Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
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Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Pearson
Rust Programming Essentials
This comprehensive video course on Rust programming provides a solid foundation in both fundamental and advanced concepts of the language, making it ideal for beginners and those looking to deepen their understanding. Learners will explore core Rust syntax, data structures, and memory management, while also delving into advanced topics like multithreading, concurrency, and real-world application development.
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Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
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Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
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Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Packt
Selenium Integration with CI/CD & Advanced Testing
This course is designed to transform your Selenium automation capabilities by diving deep into advanced testing techniques and essential integrations. Starting with Git version control, you’ll gain a strong foundation in managing your code effectively, from creating repositories to resolving merge conflicts. As you progress, the course introduces Continuous Integration and Delivery (CI/CD) with Jenkins and GitHub, showing you how to automate and streamline your testing processes. You'll configure webhooks, create Selenium jobs, and ensure a seamless workflow, making your testing framework robust and efficient. Moving forward, the focus shifts to data-driven testing using Excel, where you'll learn to harness the power of Apache POI API for reading and writing data, as well as integrating Excel with DataProviders for dynamic testing. The course also covers cross-browser testing with Selenium Grid, guiding you through setting up grid infrastructure, creating test nodes, and executing tests across different browsers and operating systems. This ensures your applications are thoroughly tested for compatibility and performance across environments. The course culminates with a deep dive into Selenium 4’s Chrome DevTools Protocol (CDP) integration, enabling you to perform advanced testing tasks like network interception, mobile simulation, and more. You’ll also explore database connections with Selenium, AutoIT for handling file uploads, and cloud-based cross-browser testing using third-party vendors like BrowserStack. By the end of this course, you'll be fully equipped to implement and manage complex automation frameworks in a professional setting. This course is ideal for automation testers, QA engineers, and software developers with a foundational understanding of Selenium and Java. Prerequisites include basic knowledge of Selenium WebDriver, core Java programming, and fundamental concepts of testing. Familiarity with Git and Jenkins is beneficial but not mandatory.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
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FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
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FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Indian Institute of Science
Sensor Technologies for Biomedical Applications
Become an expert in Sensor Technologies for Biomedical Applications with this comprehensive program designed by experts from the Indian Institute of Science - India's #1 University. Through a unique blend of theoretical knowledge and hands-on experience including lab demonstrations and a real-world capstone project, dive deep into sensor technology and gain practical experience in sensor design, characterisation, interfacing, and their applications in medical diagnostics, monitoring, and therapeutics. Master industry-relevant skills like MEMS/Nano Sensor Design & Development especially for healthcare applications, Sensor Performance Analysis, Nanofabrication, Implantable Sensors, Biomedical Device Development & Validation, Biomedical Data Analysis & Management, AI in Healthcare, and learn to navigate the regulatory and ethical landscape of biomedical devices. This program equips you with the skills and knowledge to excel in highly sought roles such as MEMS Design Engineer/Specialist, Biomedical Data Scientist/Analyst, Algorithm Engineer for MEMS, and more.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
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FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
Spark, Skew & Speed: Pipeline Performance Engineering
Slow pipelines, data skew, query bottlenecks, and cascading anomalies are not just performance problems — they are production risks. This program teaches you how to find them, fix them, and prevent them from recurring. Spark, Skew & Speed is an advanced program designed for data engineers, pipeline architects, and analytics engineers who want to build distributed data systems that perform reliably at enterprise scale. Across eight focused courses, you will master the core disciplines of pipeline performance engineering: optimizing Apache Spark jobs through partitioning and caching strategies, diagnosing and resolving data skew and shuffle inefficiencies, benchmarking competing pipeline designs, automating transformation model generation, tracing and fixing data anomalies, debugging Python pipeline failures, tuning database query performance, and making data-driven migration decisions between columnar and row-store architectures. You will work with tools and frameworks including Apache Spark, PySpark, Spark UI, SQL, and Python, applying hands-on techniques to realistic production scenarios drawn from enterprise data environments. By the end of the program, you will be equipped to build, optimize, and maintain distributed data pipelines that are fast, reliable, and ready for the demands of production analytics infrastructure.
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Technology & Coding
DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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DeepLearning.AI
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.
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TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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Technology & Coding
DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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DeepLearning.AI
TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever. Looking for a place to start? Master the foundational basics of TensorFlow with the DeepLearning.AI TensorFlow Developer Professional Certificate. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.
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LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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Technology & Coding
LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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Technology & Coding
LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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Technology & Coding
LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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Technology & Coding
LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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Technology & Coding
LearnQuest
Test-Driven Development
In this Test-Driven Development Specialization, we will take a hands-on look at Test-Driven Development by writing and implementing tests from the first module. You'll be translating user specs into unit tests, applying the Red-Green-Refactor mantra, and applying mocks in python with the unit test mock module. You'll learn to integrate best practices of test-driven development into your programming workflow and refactor legacy codebases with the help of agile methodologies. We will explore continuous integration and how to write automated tests in Python. Finally, we will work everything we've learned together to write code that contains error handlers, automated tests, and refactored functions.
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Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
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Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
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Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
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COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
UI / UX Design
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design, and offers practical, skill-based instruction centered around a visual communications perspective, rather than on one focused on marketing or programming alone. In this sequence of four courses, you will summarize and demonstrate all stages of the UI/UX development process, from user research to defining a project’s strategy, scope, and information architecture, to developing sitemaps and wireframes. You’ll learn current best practices and conventions in UX design and apply them to create effective and compelling screen-based experiences for websites or apps. User interface and user experience design is a high-demand field, but the skills and knowledge you will learn in this Specialization are applicable to a wide variety of careers, from marketing to web design to human-computer interaction. Learners enrolled in the UI/UX Design Specialization are eligible for an extended free trial (1 month) of a full product suite of UX tools from Optimal Workshop. Details are available in Course 3 of the Specialization, Web Design: Strategy and Information Architecture.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
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Technology & Coding
Coursera
User Retention Analytics
Learn the complete retention analytics lifecycle from data transformation to predictive modeling in this comprehensive 8-course specialization. You'll build expertise in cohort analysis, funnel optimization, activation metrics, and retention visualization using industry-standard tools like SQL, Python, R, and Tableau. Through hands-on projects, you'll learn to segment users by acquisition channels, identify behavioral patterns through clustering, validate activation-retention correlations, and create compelling visualizations that drive strategic decisions. This specialization bridges technical analytics skills with business strategy, enabling you to transform raw user data into actionable insights that optimize marketing spend, improve product-market fit, and boost long-term user retention across digital products.
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Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
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COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
L&T EduTech
VLSI chip design with CPS for Industrial Applications
This specialization, "Cyber Physical System for Industrial Applications," offers a thorough exploration of designing, implementing, and applying CPS technologies across industries. The courses cover essential topics such as embedded processors, wireless communication, and cybersecurity. In the first course, participants learn to design CPS with ARM Processor using Embedded C, ensuring efficiency, security, and integration with wireless communication protocols. The second course delves into CPS Design with ARM Core using Micro Python, focusing on applications in consumer products, infrastructure management, and urban planning. The third course explores CPS Applications in Mechatronics, Healthcare, EV, and Robotics, providing practical experience in developing robotic arms for industrial automation. Overall, this specialization combines theoretical knowledge with practical application, equipping participants with the skills needed to design, implement, and manage CPS solutions tailored to industrial needs, making them valuable assets in today's digital transformation landscape.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
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COURSE
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Technology & Coding
University of California San Diego
Introduction to Big Data
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
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COURSE
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Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Concurrency in Go
Learn how to implement concurrent programming in Go. Explore the roles of channels and goroutines in implementing concurrency. Topics include writing goroutines and implementing channels for communications between goroutines. Course activities will allow you to exercise Go’s capabilities for concurrent programming by developing several example programs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
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COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
DeepLearning.AI
Modèles de séquence
Cette formation vous apprendra à construire des modèles pour le langage naturel, l’audio et les autres données de séquence. Grâce à l’apprentissage profond, les algorithmes de séquence fonctionnent beaucoup mieux qu’il y a deux ans ; nous disposons donc de nombreuses applications très intéressantes en matière de reconnaissance vocale, de synthèse musicale, de chatbots, de traduction automatique, de compréhension naturelle du langage, etc. Vous allez: - Comprendre comment construire et former des réseaux neuronaux récurrents (RNN) et des variantes couramment utilisées telles que les GRU et les LSTM. - Être capable d’appliquer des modèles de séquence à des problèmes de langage naturel, y compris la synthèse de texte. - Pouvoir appliquer des modèles de séquence à des applications audio, incluant la reconnaissance vocale et la synthèse musicale. C’est le cinquième et dernier cours de la spécialisation Apprentissage profond. deeplearning.ai travaille également en partenariat avec le NVIDIA Deep Learning Institute (DLI) dans le cours 5, Modèles de séquence, afin de fournir une affectation de programmation sur la traduction automatique avec l’apprentissage en profondeur. Vous aurez la possibilité de construire un projet d’apprentissage en profondeur avec un contenu de pointe, pertinent pour l’industrie.
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COURSE
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Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
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COURSE
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Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
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Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
Programming for the Internet of Things Project
In this Capstone course, you will design a microcontroller-based embedded system. As an option, you can also build and test a system. The focus of your project will be to design the system so that it can be built on a low-cost budget for a real-world application. To complete this project you'll need to use all the skills you've learned in the course (programming microcontrollers, system design, interfacing, etc.). The project will include some core requirements, but leave room for your creativity in how you approach the project. In the end, you will produce a unique final project, suitable for showcasing to future potential employers. Note that for the three required assignments you do NOT need to purchase software and hardware to complete this course. There is an optional fourth assignment for students who wish to build and demonstrate their system using an Arduino or Raspberry Pi. Please also note that this course does not include discussion forums. Upon completing this course, you will be able to: 1. Write a requirements specification document 2. Create a system-level design 3. Explore design options 4. Create a test plan
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California, Irvine
The Arduino Platform and C Programming
This course introduces the Arduino, an open-source platform for building digital devices and interactive objects. You'll explore the Arduino board, its libraries, and the Integrated Development Environment (IDE). Learn to program Arduino using C code, control external devices via pins, and understand how shields extend functionality. This program is ideal for aspiring software developers, embedded systems engineers, and hobbyists eager to create interactive hardware projects. By the end of this course, you will be able to: - Outline Arduino board components and functions. - Program Arduino using C language fundamentals. - Debug embedded software on Arduino platforms. - Implement serial communication protocols. To be successful, a basic understanding of programming concepts is beneficial. You will use the Arduino IDE and C programming language.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
AI For Business
This specialization will provide learners with the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning and the various areas in which you can deploy them to support your business. You'll cover ethics and risks of AI, designing governance frameworks to fairly apply AI, and also cover people management in the fair design of HR functions within Machine Learning. You'll also learn effective marketing strategies using data analytics, and how personalization can enhance and prolong the customer journey and lifecycle. Finally, you will hear from industry leaders who will provide you with insights into how AI and Big Data are revolutionizing the way we do business. By the end of this specialization, you will be able to implement ethical AI strategies for people management and have a better understanding of the relationship between data analytics, artificial intelligence, and machine learning. You will leave this specialization with insight into how these tools can shape and influence how you manage your business. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource,". You can find Professor Hosanagar's book on his personal website or at Penguin Randomhouse.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths.\n\nThis Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accelerated Computer Science Fundamentals
Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics
This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of California San Diego
Advanced Algorithms and Complexity
In previous courses of our online specialization you've learned the basic algorithms, and now you are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them in practice. We finish with a soft introduction to streaming algorithms that are heavily used in Big Data processing. Such algorithms are usually designed to be able to process huge datasets without being able even to store a dataset.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Rice University
Algorithmic Thinking (Part 1)
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms. Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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COURSE
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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COURSE
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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COURSE
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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COURSE
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Technology & Coding
University of Colorado Boulder & University of Colorado System
Algorithms for Battery Management Systems
In this specialization, you will learn the major functions that must be performed by a battery management system, how lithium-ion battery cells work and how to model their behaviors mathematically, and how to write algorithms (computer methods) to estimate state-of-charge, state-of-health, remaining energy, and available power, and how to balance cells in a battery pack.
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Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
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Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
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Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
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COURSE
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Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
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COURSE
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Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
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COURSE
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Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Applied Plotting, Charting & Data Representation in Python
This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
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Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
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Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
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Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
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Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
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Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
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Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Applying Data Analytics in Finance
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
Politecnico di Milano
Artificial Intelligence: an Overview
This Specialization is intended for beginners seeking to enter the artificial intelligence world. Through five courses, you will cover artificial intelligence technical groundings (including machine learning and technologies), ethical and legal issues, which will give you a clear picture of what artificial intelligence is and what opportunities artificial intelligence will provide in the next future.
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado System
Asymmetric Cryptography and Key Management
Welcome to Asymmetric Cryptography and Key Management! In asymmetric cryptography or public-key cryptography, the sender and the receiver use a pair of public-private keys, as opposed to the same symmetric key, and therefore their cryptographic operations are asymmetric. This course will first review the principles of asymmetric cryptography and describe how the use of the pair of keys can provide different security properties. Then, we will study the popular asymmetric schemes in the RSA cipher algorithm and the Diffie-Hellman Key Exchange protocol and learn how and why they work to secure communications/access. Lastly, we will discuss the key distribution and management for both symmetric keys and public keys and describe the important concepts in public-key distribution such as public-key authority, digital certificate, and public-key infrastructure. This course also describes some mathematical concepts, e.g., prime factorization and discrete logarithm, which become the bases for the security of asymmetric primitives, and working knowledge of discrete mathematics will be helpful for taking this course; the Symmetric Cryptography course (recommended to be taken before this course) also discusses modulo arithmetic. This course is cross-listed and is a part of the two specializations, the Applied Cryptography specialization and the Introduction to Applied Cryptography specialization.
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Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
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Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
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COURSE
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Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
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COURSE
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Technology & Coding
University of Colorado System
Basic Cryptography and Programming with Crypto API
In this MOOC, we will learn the basic concepts and principles of crytography, apply basic cryptoanalysis to decrypt messages encrypted with mono-alphabetic substitution cipher, and discuss the strongest encryption technique of the one-time-pad and related quantum key distribution systems. We will also learn the efficient symmetric key cryptography algorithms for encrypting data, discuss the DES and AES standards, study the criteria for selecting AES standard, present the block cipher operating modes and discuss how they can prevent and detect the block swapping attacks, and examine how to defend against replay attacks. We will learn the Diffie-Hellman Symmetric Key Exchange Protocol to generate a symmetric key for two parties to communicate over insecure channel. We will learn the modular arithmetic and the Euler Totient Theorem to appreciate the RSA Asymmetric Crypto Algorithm, and use OpenSSL utility to realize the basic operations of RSA Crypto Algorithm. Armed with these knowledge, we learn how to use PHP Crypto API to write secure programs for encrypting and decrypting documents and for signing and verify documents. We then apply these techniques to enhance the registration process of a web site which ensures the account created is actually requested by the owner of the email account.
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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COURSE
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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COURSE
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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COURSE
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Technology & Coding
University of California, Santa Cruz
Bayesian Statistics
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
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Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
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Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
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Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
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Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
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Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
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Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making.\n\nThe courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include:\n\nData strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Business Writing
Writing well is one of the most important skills you can develop to be successful in the business world. Over seventy companies and thirty thousand students--from professional writers to new employees to non-native English speakers to seasoned executives--have used the techniques in Business Writing to power their ability to communicate and launch their ideas. This course will teach you how to apply the top ten principles of good business writing to your work, how to deploy simple tools to dramatically improve your writing, and how to execute organization, structure, and revision to communicate more masterfully than ever. From the very first lesson, you'll be able to apply your new learning immediately to your work and improve your writing today. Your ideas are powerful. Learn how to deliver them with the clarity and impact they deserve. "Thank you for giving me the knowledge I need in life. [Business Writing] was helpful, life changing, and has made a huge impact in my writing." -- Message from a Business Writing student The principles you'll learn in this course enable you to become a great business writer. They also provide the foundation for moving into Graphic Design and Successful Presentation, so that you can unleash your best professional self whenever--and however--you present your ideas in the workplace. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado System
Classical Cryptosystems and Core Concepts
Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020. In this specialization, you will learn basic security issues in computer communications, classical cryptographic algorithms, symmetric-key cryptography, public-key cryptography, authentication, and digital signatures. These topics should prove especially useful to you if you are new to cybersecurity Course 1, Classical Cryptosystems, introduces you to basic concepts and terminology related to cryptography and cryptanalysis. It is recommended that you have a basic knowledge of computer science and basic math skills such as algebra and probability.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of Illinois Urbana-Champaign
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of California San Diego
Combinatorics and Probability
Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics. In this online course we discuss most standard combinatorial settings that can help to answer questions of this type. We will especially concentrate on developing the ability to distinguish these settings in real life and algorithmic problems. This will help the learner to actually implement new knowledge. Apart from that we will discuss recursive technique for counting that is important for algorithmic implementations. One of the main ‘consumers’ of Combinatorics is Probability Theory. This area is connected with numerous sides of life, on one hand being an important concept in everyday life and on the other hand being an indispensable tool in such modern and important fields as Statistics and Machine Learning. In this course we will concentrate on providing the working knowledge of basics of probability and a good intuition in this area. The practice shows that such an intuition is not easy to develop. In the end of the course we will create a program that successfully plays a tricky and very counterintuitive dice game. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Comparing Genes, Proteins, and Genomes (Bioinformatics III)
Once we have sequenced genomes in the previous course, we would like to compare them to determine how species have evolved and what makes them different. In the first half of the course, we will compare two short biological sequences, such as genes (i.e., short sequences of DNA) or proteins. We will encounter a powerful algorithmic tool called dynamic programming that will help us determine the number of mutations that have separated the two genes/proteins. In the second half of the course, we will "zoom out" to compare entire genomes, where we see large scale mutations called genome rearrangements, seismic events that have heaved around large blocks of DNA over millions of years of evolution. Looking at the human and mouse genomes, we will ask ourselves: just as earthquakes are much more likely to occur along fault lines, are there locations in our genome that are "fragile" and more susceptible to be broken as part of genome rearrangements? We will see how combinatorial algorithms will help us answer this question. Finally, you will learn how to apply popular bioinformatics software tools to solve problems in sequence alignment, including BLAST.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
Stanford University
Cryptography I
Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key. Throughout the course participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.
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Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
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Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Washington
Data Manipulation at Scale: Systems and Algorithms
Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
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Technology & Coding
University of Colorado Boulder
Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project. Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science Methods for Quality Improvement
Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability. By completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field. Learners are encouraged to complete this specialization in the order the courses are presented. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
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Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
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Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
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Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
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Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
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Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
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Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Whizlabs
Data Science and Machine Learning Engineering on Microsoft Azure
The Exam Prep DP-100: Microsoft Certified Azure Data Scientist Associate course is designed for professionals aiming to apply data science and machine learning to Azure workloads. This course equips learners with the skills to design, implement, and optimize machine learning solutions using Azure Machine Learning, MLflow, and Azure AI services. Participants will gain hands-on experience in data ingestion, preparation, model training, deployment, and monitoring. The specialization is divided into four key courses: Azure ML: Designing & Preparing Machine Learning Solutions Azure ML: Explore & Configure the Machine Learning Workspace Azure ML: Deploying, Managing, and Experimenting with Models Azure AI & ML: Optimize Language Models for AI Applications These courses are further divided into Modules, Lessons, and Video Items. All the courses have a set of Practice and Graded assignments available that test the candidate's ability to understand the concepts and grasp the topics discussed in the courses. This course aims to achieve the Microsoft Certified: Azure Data Scientist Associate Certification. This certification validates your ability to: Design and implement a data science environment. Prepare and explore data for ML workflows. Train and evaluate models using MLflow & Azure AI services. Deploy and monitor ML models for scalable AI applications. By earning this certification, you have positioned yourself as a skilled Azure Data Scientist
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Washington
Data Science at Scale - Capstone Project
In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.
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Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
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Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
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Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures
A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this online course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures. A few examples of questions that we are going to cover in this class are the following: 1. What is a good strategy of resizing a dynamic array? 2. How priority queues are implemented in C++, Java, and Python? 3. How to implement a hash table so that the amortized running time of all operations is O(1) on average? 4. What are good strategies to keep a binary tree balanced? You will also learn how services like Dropbox manage to upload some large files instantly and to save a lot of storage space!
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Data Structures and Algorithms
Computer science legend Donald Knuth once said “I don’t understand things unless I try to program them.” We also believe that the best way to learn an algorithm is to program it. However, many excellent books and online courses on algorithms, that excel in introducing algorithmic ideas, have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill that you have to master at your next job interview. We tried to fill this gap by forming a diverse team of instructors that includes world-leading experts in theoretical and applied algorithms at UCSD (Daniel Kane, Alexander Kulikov, and Pavel Pevzner) and a former software engineer at Google (Neil Rhodes). This unique combination of skills makes this Specialization different from other excellent MOOCs on algorithms that are all developed by theoretical computer scientists. While these MOOCs focus on theory, our Specialization is a mix of algorithmic theory/practice/applications with software engineering. You will learn algorithms by implementing nearly 100 coding problems in a programming language of your choice. To the best of knowledge, no other online course in Algorithms comes close to offering you a wealth of programming challenges (and puzzles!) that you may face at your next job interview. We invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
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Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
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Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
University of Colorado Boulder
Databases for Data Scientists
Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development. In this specialization you will learn about database design, database software fundamentals, and how to use the Structured Query Language (SQL) to work with databases. The specialization, will conclude with an overview of future trends in databases, including non-relational databases (NoSQL) and Big Data. Upon completion of this specialization you will be well prepared to design and create efficient and effective relational databases, fill them with data, and work with them using SQL.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
University of Illinois at Urbana-Champaign
Deep Learning for Healthcare
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
Google Cloud
Developing Applications with Google Cloud en Français
Dans cette spécialisation, les développeurs d'applications apprennent à concevoir, développer et déployer des applications qui intègrent de manière transparente les services gérés de Google Cloud Platform (GCP). Grâce à une combinaison de présentations, de démonstrations et d'ateliers pratiques, les participants apprennent à utiliser les services GCP et les API de machine learning pré-formées pour créer des applications cloud natives sécurisées, évolutives et intelligentes. Les apprenants peuvent choisir de terminer les travaux pratiques dans leur langage préféré : Node.js, Java ou Python. Ce cours est destiné aux développeurs d'applications qui souhaitent créer des applications cloud natives ou reconcevoir des applications existantes qui s'exécuteront sur Google Cloud Platform.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Developing Industrial Internet of Things
The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Stanford University
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Effective Communication Capstone Project
In the Effective Communication Capstone learners apply the lessons of Business Writing, Graphic Design, and Successful Presentation to create a portfolio of work that represents their mastery of writing, design, and speaking and that expresses their personal brand. The portfolio includes three individual elements—a written memo, a slide deck, and a presentation—integrated around a single topic. We provide the elements for a basic capstone, but we also invite our learners to create their own project if they so choose. This self-designed "Challenge Capstone" allows learners to engage meaningfully in their world by choosing a project relevant to their current job or by volunteering to write, design, and speak for a social organization of their choice. By successfully undertaking the Capstone, learners will accomplish three main goals: 1. They will hone their writing, design, and speaking skills and build a portfolio for a job search and/or professional application; 2. They will shape these skills into a unique brand identity; and, if they choose, 3. They will undertake a transformative effect in the world around them. Ultimately, the importance of Capstone portfolio is greater than the sum of its individual parts. Its true goal lies in each student’s personal transformation and expression of their best selves. We invite you to unleash your skills and we can't wait to see the results! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Effective Programming in Scala
Scala is an expressive, versatile, and safe programming language. In this course, you will learn how to get the most out of Scala to solve common programming tasks such as modeling business domains, breaking down complex problems into simpler problems, manipulating data, or running parallel tasks. Along the journey, you will also learn the best practices for writing high-quality code that scales to large applications, how to handle errors, how to write tests, and how to leverage a productive development environment. This comprehensive, hands-on, course aims at leveling up your programming skills by embracing both functional programming and object-oriented programming. You will become familiar with the standard library and the common patterns of code used in the real world. Each week contains about 1h30 of video lectures. Each lecture is a ~10 min video focused on a specific skill or concept. We always start by looking at concrete problems, and then explain how language features or libraries make you more productive to solve these problems in general. Lectures are generally followed by a quiz to assess your progress. At the end of each week, a graded assignment inspired by real-world applications will give you an opportunity to put things in practice. The course covers Scala 3, and it mentions the differences with Scala 2.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Embedding Sensors and Motors
The courses in this specialization can also be taken for academic credit as ECEA 5340-5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. Embedding Sensors and Motors will introduce you to the design of sensors and motors, and to methods that integrate them into embedded systems used in consumer and industrial products. You will gain hands-on experience with the technologies by building systems that take sensor or motor inputs, and then filter and evaluate the resulting data. You will learn about hardware components and firmware algorithms needed to configure and run sensors and motors in embedded solutions.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
Columbia University
Financial Engineering and Risk Management
This specialization is intended for aspiring learners and professionals seeking to hone their skills in the quantitative finance area. Through a series of 5 courses, we will cover derivative pricing, asset allocation, portfolio optimization as well as other applications of financial engineering such as real options, commodity and energy derivatives and algorithmic trading. Those financial engineering topics will prepare you well for resolving related problems, both in the academic and industrial worlds.
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Foundations of Data Structures and Algorithms
Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
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Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
ESSEC Business School
Foundations of marketing analytics
Who is this course for? This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing. You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage. It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on. But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future. That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
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Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
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Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
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COURSE
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Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
NIIT
Frontend Development using React
If innovation and creativity in technology attracts you and developing impressive webpages are your passion, then this specialization is for you. Front-end developers are skilled professionals who are experts in combining the art of designing with the science of programming. The skills acquired in this field presents copious opportunities for individuals like you in the field of web application development. This Specialization transforms learners with no programming background into front-end Web developers who can build highly engaging consumer-facing, rich front-end Single Page Application (SPA).
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Functional Program Design in Scala
In this course you will learn how to apply the functional programming style in the design of larger Scala applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world. Finally, you will learn how to leverage the ability of the compiler to infer values from types. Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming. Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity with using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.
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Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
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Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
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Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Colorado System
Fundamentals of Network Communication
In this course, we trace the evolution of networks and identify the key concepts and functions that form the basis for layered architecture. We introduce examples of protocols and services that are familiar to the students, and we explain how these services are supported by networks. Further, we explain fundamental concepts in digital communication, and focus on error control techniques that include parity check, polynomial code, and Internet checksum. Students will be required to have some previous programming experience in C-programming (C++/Java), some fundamental knowledge of computer organization and IT architecture and a background in computer science is a plus.
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Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
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Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Sequencing (Bioinformatics II)
You may have heard a lot about genome sequencing and its potential to usher in an era of personalized medicine, but what does it mean to sequence a genome? Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. In the first half of the course, we will see that biologists cannot read the 3 billion nucleotides of a human genome as you would read a book from beginning to end. However, they can read shorter fragments of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces in what amounts to the largest jigsaw puzzle ever put together. In the second half of the course, we will discuss antibiotics, a topic of great relevance as antimicrobial-resistant bacteria like MRSA are on the rise. You know antibiotics as drugs, but on the molecular level they are short mini-proteins that have been engineered by bacteria to kill their enemies. Determining the sequence of amino acids making up one of these antibiotics is an important research problem, and one that is similar to that of sequencing a genome by assembling tiny fragments of DNA. We will see how brute force algorithms that try every possible solution are able to identify naturally occurring antibiotics so that they can be synthesized in a lab. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium that has acquired antibiotics resistance.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Graphic Design
Welcome to Graphic Design, the second course in the Effective Communications Specialization. Over 70 different companies have provided this specialization to their employees as a resource for internal professional development. Why? Because employers know that effective visual communication is the key to attracting an audience, building a relationship, and closing the sale. This practical course gives you the tools to create professional looking PowerPoints, reports, resumes, and presentations. Using a set of best practices refined through years of experience, you’ll: • make your work look fresh and inspired. • apply simple design “tricks” to begin any project with confidence and professionalism. • receive and respond to criticism and revise your project from good to great. "This course is fantastic. It teaches a great amount of starter graphic design information but it is broken down into easily understood videos and quizzes. The quality was top notch and the interaction was as good as you would get in a brick and mortar school. I did not feel like I was missing anything by taking it online." - a recent Graphic Design student All of the course assignments can be completed with basic presentation software such as Microsoft PowerPoint, Google Slides, or Apple Keynote. You’ll also have opportunities to explore and apply more sophisticated tools, such as Adobe Photoshop, Illustrator, and InDesign. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
IBM
IBM AI Enterprise Workflow
This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Applied Business Analytics
Nearly every aspect of business is affected by data analytics. For businesses to capitalize on data analytics, they need leaders who understand the business analytic workflow. This course addresses the human skills gap by providing a foundational set of data processing skills that can be applied to many business settings. In this course you will use Python, a widely adopted data analytics language, to efficiently prepare business data for analytic tools such as algorithms and visualizations. Cleaning, transforming, aggregating, and reshaping data is a critical, but inconspicuous step in the business analytic workflow. As you learn how to use Python to prepare data for analysis, you will gain experience using integrated development environments (IDEs) that simplify coding, support data exploration, and help you share results effectively. As you learn about the business analytics workflow you will also consider the interplay between business principles and data analytics. Specifically, you will explore how delegation, control, and feasibility influence the way in which data is processed. You will also be introduced to examples of business problems that can be solved with data automation and analytics, and methods for communicating data analytic results that do not require copying and pasting from one platform to another.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
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FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
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Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
California Institute of the Arts
Introduction to Game Design
Study the foundational mechanics, rules, and conceptual underpinnings of game design. Define what makes a game compelling through industry-standard documentation and structured conceptual frameworks. Develop and describe original game concepts through four iterative assignments designed to bridge the gap between abstract ideas and playable mechanics—no programming experience required.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
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Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
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Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
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Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
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Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
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Technology & Coding
University of Pennsylvania
Introduction to Programming with Python and Java
This Specialization starts out by teaching basic concepts in Python and ramps up to more complex subjects such as object-oriented programming and data structures in Java. By the time learners complete this series of four courses, they will be able to write fully-functional programs in both Python and Java, two of the most well-known and frequently used programming languages in the world today. Introduction to Programming with Python and Java is for students and professionals who have minimal or no prior programming exposure. It’s for motivated learners who have experience with rigorous coursework, and are looking to gain a competitive edge in advancing their career. It’s for folks who are thinking about applying to the University of Pennsylvania’s online Master of Computer and Information Technology degree and want to sample some of the lecture videos and content from the first course in the program. We hope this Specialization is for you. Topics in this Specialization include language syntax, style, programming techniques, and coding conventions. Learn about best practices and good code design, code testing and test-driven development, code debugging, code and program documentation, and computational thinking.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Stanford University
Introduction to Statistics
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning. Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Google Cloud & New York Institute of Finance
Introduction to Trading, Machine Learning & GCP
In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Build a Recommendation System
Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to: 1. Read in and parse data into lists and maps; 2. Calculate average ratings; 3. Calculate how similar a given rater is to another user based on ratings; and 4. Recommend movies to a given user based on ratings. 5. Display recommended movies for a given user on a webpage.
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Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
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Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Java Programming: Principles of Software Design
Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
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Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
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Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
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Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
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Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
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COURSE
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Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Northwestern University
Leadership Through Marketing
The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.
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Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
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Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
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COURSE
FREE TRIAL
Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
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COURSE
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Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
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FREE TRIAL
Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
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COURSE
FREE TRIAL
Technology & Coding
University of Washington
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
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FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
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Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
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Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Mastering the Software Engineering Interview
You’ve hit a major milestone as a computer scientist and are becoming a capable programmer. You now know how to solve problems, write algorithms, and analyze solutions; and you have a wealth of tools (like data structures) at your disposal. You may now be ready for an internship or (possibly) an entry-level software engineering job. But can you land the internship/job? It depends in part on how well you can solve new technical problems and communicate during interviews. How can you get better at this? Practice! With the support of Google’s recruiting and engineering teams we’ve provided tips, examples, and practice opportunities in this course that may help you with a number of tech companies. We’ll assist you to organize into teams to practice. Lastly, we’ll give you basic job search advice, and tips for succeeding once you’re on the job.
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Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
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Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Minnesota
Matrix Factorization and Advanced Techniques
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 4: Robot Motion Planning and Control
Do you want to know how robots work? Are you interested in robotics as a career? Are you willing to invest the effort to learn fundamental mathematical modeling techniques that are used in all subfields of robotics? If so, then the "Modern Robotics: Mechanics, Planning, and Control" specialization may be for you. This specialization, consisting of six short courses, is serious preparation for serious students who hope to work in the field of robotics or to undertake advanced study. It is not a sampler. In Course 4 of the specialization, Robot Motion Planning and Control, you will learn key concepts of robot motion generation: planning a motion for a robot in the presence of obstacles, and real-time feedback control to track the planned motion. Chapter 10, Motion Planning, of the "Modern Robotics" textbook covers foundational material like C-space obstacles, graphs and trees, and graph search, as well as classical and modern motion planning techniques, such as grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Chapter 11, Robot Control, covers motion control, force control, and hybrid motion-force control. This course follows the textbook "Modern Robotics: Mechanics, Planning, and Control" (Lynch and Park, Cambridge University Press 2017). You can purchase the book or use the free preprint pdf. You will build on a library of robotics software in the language of your choice (among Python, Mathematica, and MATLAB) and use the free cross-platform robot simulator V-REP, which allows you to work with state-of-the-art robots in the comfort of your own home and with zero financial investment.
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Northwestern University
Modern Robotics, Course 6: Capstone Project, Mobile Manipulation
The capstone project of the Modern Robotics specialization is on mobile manipulation: simultaneously controlling the motion of a wheeled mobile base and its robot arm to achieve a manipulation task. This project integrates several topics from the specialization, including trajectory planning, odometry for mobile robots, and feedback control. Beginning from the Modern Robotics software library provided to you (written in Python, Mathematica, and MATLAB), and software you have written for previous courses, you will develop software to plan and control the motion of a mobile manipulator to perform a pick and place task. You will test your software on the KUKA youBot, a mobile manipulator consisting of an omnidirectional mecanum-wheel mobile base, a 5-joint robot arm, and a gripper. The state-of-the-art, cross-platform V-REP robot simulator will be used to simulate the task.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
University of California San Diego
Number Theory and Cryptography
A prominent expert in the number theory Godfrey Hardy described it in the beginning of 20th century as one of the most obviously useless branches of Pure Mathematics”. Just 30 years after his death, an algorithm for encryption of secret messages was developed using achievements of number theory. It was called RSA after the names of its authors, and its implementation is probably the most frequently used computer program in the world nowadays. Without it, nobody would be able to make secure payments over the internet, or even log in securely to e-mail and other personal services. In this course we will start with the basics of the number theory and get to cryptographic protocols based on it. By the end, you will be able to apply the basics of the number theory to encrypt and decrypt messages, and to break the code if one applies RSA carelessly. You will even pass a cryptographic quest! As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in IT, starting from motivated high school students.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Parallel programming
With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Rice University
Principles of Computing (Part 1)
This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming. The main focus of the class will be programming weekly mini-projects in Python that build upon the mathematical and programming principles that are taught in the class. To keep the class fun and engaging, many of the projects will involve working with strategy-based games. In part 1 of this course, the programming aspect of the class will focus on coding standards and testing. The mathematical portion of the class will focus on probability, combinatorics, and counting with an eye towards practical applications of these concepts in Computer Science. Recommended Background - Students should be comfortable writing small (100+ line) programs in Python using constructs such as lists, dictionaries and classes and also have a high-school math background that includes algebra and pre-calculus.
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Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
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Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
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Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
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COURSE
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Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
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Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
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Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
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Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Foundations with JavaScript, HTML and CSS
Learn foundational programming concepts (e.g., functions, for loops, conditional statements) and how to solve problems like a programmer. In addition, learn basic web development as you build web pages using HTML, CSS, JavaScript. By the end of the course, will create a web page where others can upload their images and apply image filters that you create. After completing this course, you will be able to: 1. Think critically about how to solve a problem using programming; 2. Write JavaScript programs using functions, for loops, and conditional statements; 3. Use HTML to construct a web page with paragraphs, divs, images, links, and lists; 4. Add styles to a web page with CSS IDs and classes; and 5. Make a web page interactive with JavaScript commands like alert, onClick, onChange, adding input features like an image canvas, button, and slider.
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Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
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Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
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COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Duke University
Programming Fundamentals
Embark on your programming journey! This introductory course teaches you the fundamental principles of programming in C that are applicable to any language you might want to learn. Master a powerful seven-step problem-solving process for developing effective algorithms. Learn to read and understand code, transforming complex challenges into manageable solutions. No prior experience needed. Develop core skills for software development and enhance your career prospects in diverse fields. By the end of this course, you will be able to develop algorithms that are specific and correct.
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
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Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
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FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
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COURSE
FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
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COURSE
FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
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COURSE
FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
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COURSE
FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
École Polytechnique Fédérale de Lausanne
Programming Reactive Systems (Scala 2 version)
Reactive programming is a set of techniques for implementing scalable, resilient and responsive systems as per the Reactive Manifesto. Such systems are based on asynchronous message-passing, and their basic building-blocks are event handlers. This course teaches how to implement reactive systems in Scala and Akka by using high-level abstractions, such as actors, asynchronous computations, and reactive streams. You will learn how to: - use, transform and sequence asynchronous computations using Future values - write concurrent reactive systems based on Actors and message passing, using untyped Akka and Akka Typed - design systems resilient to failures - implement systems that can scale out according to a varying workload - transform and consume infinite and intermittent streams of data with Akka Stream in a non-blocking way - understand how back-pressure controls flows of data
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
IBM
Python Project for Data Engineering
Showcase your Python skills in this Data Engineering Project! This short course is designed to apply your basic Python skills through the implementation of various techniques for gathering and manipulating data. You will take on the role of a Data Engineer by extracting data from multiple sources, and converting the data into specific formats and making it ready for loading into a database for analysis. You will also demonstrate your knowledge of web scraping and utilizing APIs to extract data. By the end of this hands-on project, you will have shown your proficiency with important skills to Extract Transform and Load (ETL) data using an IDE, and of course, Python Programming. Upon completion of this course, you will also have a great new addition to your portfolio! PRE-REQUISITE: **Python for Data Science, AI and Development** course from IBM is a pre-requisite for this project course. Please ensure that before taking this course you have either completed the Python for Data Science, AI and Development course from IBM or have equivalent proficiency in working with Python and data. NOTE: This course is not intended to teach you Python and does not have too much new instructional content. It is intended for you to mostly apply prior Python knowledge.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python استخدام قواعد البيانات مع
سيتعرف الطلاب خلال هذه الدورة على أساسيات لغة الاستعلام البنوية (SQL) وتصميم قاعدة البيانات الأساسية لتخزين البيانات كجزءٍ من جمع البيانات متعددة الخطوات وتحليلها ومعالجتها. تستخدم الدورة التدريبية SQLite3 قاعدة بيانات لها. نعمل أيضًا على إنشاء متتبعات الويب وعمليات جمع البيانات متعددة الخطوات وتصورها. نستخدم مكتبة D3.js لإجراء تصور البيانات الأساسية. تتناول هذه الدورة الفصلين 14 و15 من كتاب "Python for Everybody". كي تجتاز هذه الدورة، ينبغي أن تكون على دراية بالمواد المذكورة في الفصل الأول إلى الفصل 13 من الكتاب الدراسي والدورات الثلاث الأولى في هذا التخصص. تتناول هذه الدورة Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Technology & Coding
University of Michigan
Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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Python بُنى بيانات
ستقدم هذه الدورة التدريبية بُنى البيانات الأساسية للغة برمجة Python. وسوف نتجاوز أساسيات البرمجة الإجرائية ونستكشف الكيفية التي يمكننا من خلالها استخدام بُنى بيانات Python المضمَّنة، مثل القوائم والقواميس والمجموعات لإجراء تحليل معقد بشكل متزايد للبيانات. ستغطي هذه الدورة التدريبية الفصول من 6 إلى 10 من كتاب «Python للجميع» النصي. وستغطي هذه الدورة التدريبية Python 3.
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NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
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Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
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Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
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Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
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COURSE
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Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
NIIT
RESTful Microservices Using Node.js and Express
Backend refers to the server side of development. Here, the primary focus is on how a website works. Node.js is considered efficient for the development of backend applications as it brings event-driven programming and enables development of fast and efficient web servers in JavaScript. Developers can create scalable servers by using a simplified model of event-driven programming that uses call-backs to signal completion of a task.
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Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
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Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
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Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
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COURSE
FREE TRIAL
Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
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COURSE
FREE TRIAL
Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Wesleyan University
Regression Modeling in Practice
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
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COURSE
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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COURSE
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Self-Driving Cars
Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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COURSE
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Software Architecture for Big Data
This specialization is for software engineers interested in the principles of building and architecting large software systems that use big data. Through three courses you will learn about how to build and architect performant distributed systems from industry experts at Initial Capacity. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
University of Colorado Boulder
Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
LearnQuest
Swift 5 iOS Application Developer
This program is intended for anyone who wants to learn how to develop Apps using Swift and iOS. Through four courses, you will learn topics beginning with the absolute basics and ending with selling your apps on the app store. This program provides the skills you'll need to advance your programming career and seek employment in Swift and iOS application development. Throughout this hands-on program, you'll have the opportunity to practice key job skills. You'll learn about the Swift language and how to code iOS applications. You'll create user interfaces and interact with user and system data using tables and data persistence. You'll develop fully functional applications and learn how to monetize them with in-app add, purchases, and subscriptions. By the end of this Professional Certificate program, you will have completed several projects showcasing your proficiency in Swift 5 and iOS programming, and you will have developed the skills necessary to begin a career as a Swift and/or iOS application developer. You will also be able to share evidence of your success with your professional network and potential employers.
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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COURSE
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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COURSE
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Technology & Coding
University of Colorado Boulder
Text Marketing Analytics
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
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Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
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Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
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Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
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COURSE
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Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Indian School of Business
Trading Basics
The purpose of this course is to equip you with the knowledge required to comprehend the financial statements of a company and understand the various transactions that take place in the stock market so that you can replicate the strategies discovered by the extant academic literature. The first part of the course provides a brief introduction to financial statements and various common filings of firms. You will learn how to obtain information regarding a company's performance from them and use the information to build trading strategies. Next, you are taught basic asset pricing theories so that you will be able to calculate the expected returns of a stock or a portfolio. Finally, you will be introduced to the actual functioning of asset markets, type of players in the market, different types of orders and the efficient ways and opportune time to execute them, trading costs and ways of minimizing them, the concept of liquidity .etc. This knowledge is required to develop efficient algorithm to execute various trading strategies.
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Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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COURSE
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Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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COURSE
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Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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Technology & Coding
University of Illinois Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
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Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Unordered Data Structures
The Unordered Data Structures course covers the data structures and algorithms needed to implement hash tables, disjoint sets and graphs. These fundamental data structures are useful for unordered data. For example, a hash table provides immediate access to data indexed by an arbitrary key value, that could be a number (such as a memory address for cached memory), a URL (such as for a web cache) or a dictionary. Graphs are used to represent relationships between items, and this course covers several different data structures for representing graphs and several different algorithms for traversing graphs, including finding the shortest route from one node to another node. These graph algorithms will also depend on another concept called disjoint sets, so this course will also cover its data structure and associated algorithms.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Google Cloud & New York Institute of Finance
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it. To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Using Python to Access Web Data
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
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Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
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Technology & Coding
University of Michigan
Uso de Python para Acceder a Datos Web
En este curso, aprenderás cómo la Internet se puede convertir en una fuente de datos. Para esto, rasparemos, analizaremos y leeremos los datos web y también accederemos datos mediante APIs web. Trabajaremos con formatos de datos HTML, XML y JSON en Python. Este curso abarcará los capítulos 11-13 del libro de texto "Python for Everybody". Para tener éxito en este curso, debes estar familiarizado con el material que se incluye en los capítulos 1-10 del libro de texto y los dos primeros cursos de esta especialización. Estos temas incluyen variables y expresiones, ejecución condicional (bucles, ramificación y try/except), funciones, estructuras de datos de Python (cadenas, listas, diccionarios y tuplas) y manipulación de archivos. Este curso abarca Python 3.
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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COURSE
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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COURSE
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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COURSE
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Technology & Coding
University of Colorado Boulder
Vital Skills for Data Science
Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo image by JJ Ying on Unsplash
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Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
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Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
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Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
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COURSE
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Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Toronto
iOS App Development Basics
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Michigan
Структуры данных Python
В данном курсе описываются основные структуры данных языка программирования Python. Будут рассмотрены основы процедурного программирования, а также способы использования встроенных структур данных Python, например, списков, словарей и кортежей для выполнения сложного анализа данных. В данном курсе рассматриваются главы 6-10 учебника «Python для всех». В этом курсе речь идет о языке Python 3.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Applications in People Management
In this course, you will learn about Artificial Intelligence and Machine Learning as it applies to HR Management. You will explore concepts related to the role of data in machine learning, AI application, limitations of using data in HR decisions, and how bias can be mitigated using blockchain technology. Machine learning powers are becoming faster and more streamlined, and you will gain firsthand knowledge of how to use current and emerging technology to manage the entire employee lifecycle. Through study and analysis, you will learn how to sift through tremendous volumes of data to identify patterns and make predictions that will be in the best interest of your business. By the end of this course, you'll be able to identify how you can incorporate AI to streamline all HR functions and how to work with data to take advantage of the power of machine learning.
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
AI Strategy and Governance
In this course, you will discover AI and the strategies that are used in transforming business in order to gain a competitive advantage. You will explore the multitude of uses for AI in an enterprise setting and the tools that are available to lower the barriers to AI use. You will get a closer look at the purpose, function, and use-cases for explainable AI. This course will also provide you with the tools to build responsible AI governance algorithms as faculty dive into the large datasets that you can expect to see in an enterprise setting and how that affects the business on a greater scale. Finally, you will examine AI in the organizational structure, how AI is playing a crucial role in change management, and the risks with AI processes. By the end of this course, you will learn different strategies to recognize biases that exist within data, how to ensure that you maintain and build trust with user data and privacy, and what it takes to construct a responsible governance strategy. For additional reading, Professor Hosanagar's book "A Human’s Guide to Machine Intelligence" can be used as an additional resource for more extensive information on topics covered in this module.
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Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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FREE TRIAL
Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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FREE TRIAL
Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
IBM
AI Workflow: AI in Production
This is the sixth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning. You will build your own API in a Docker container and learn how to manage containers with Kubernetes. The course also introduces several other tools in the IBM ecosystem designed to help deploy or maintain models in production. The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow. By the end of this course you will be able to: 1. Use Docker to deploy a flask application 2. Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3. Discuss basic Kubernetes terminology 4. Deploy a scalable web application on Kubernetes 5. Discuss the different feedback loops in AI workflow 6. Discuss the use of unit testing in the context of model production 7. Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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FREE TRIAL
Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: Enterprise Model Deployment
This is the fifth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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COURSE
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Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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COURSE
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Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Advanced Data Structures, RSA and Quantum Algorithms
Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS) degrees offered on the Coursera platform. This fully accredited graduate degree offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Whizlabs
Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Whizlabs
Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Whizlabs
Azure Compute and Application Architecture Solutions
Azure Compute Solutions introduces learners to core compute services in Azure. This is the third course in the Exam Prep AZ-305: Microsoft Certified Azure Solutions Architect Expert specialization. The course covers Azure Virtual Machines, Azure Container Apps, ACI, AKS, App Service, App Configuration, Azure Functions, and Azure Batch. Key topics include serverless computing, containerized deployments, and batch processing, with demos on Azure Functions bindings and Batch exception handling. This course is structured into two modules, each containing Lessons and Video Lectures. Learners will engage with approximately 5:00-6:30 hours of video content, covering both theoretical concepts and hands-on practice. Each module is supplemented with quizzes to assess learners' understanding and reinforce key concepts. Course Modules: Module 1: Building Scalable Solutions with Azure Compute and Batch Processing Module 2: Azure Application Architecture Solutions By the end of this course, a learner will be able to: - Describe and deploy key Azure compute services such as Virtual Machines, Containers, and App Services. - Implement serverless solutions using Azure Functions with various input/output bindings. - Utilize Azure Batch for high-scale parallel and batch processing with robust error handling. - Design messaging architectures using Azure Service Bus and Queue Storage for decoupled communication. - Implement event-driven solutions with Azure Event Grid, Event Hubs, and Stream Analytics. - Recommend and integrate Azure API and messaging solutions to build scalable and resilient cloud applications. This course is intended for cloud developers and solution architects looking to build and deploy scalable, event-driven, and compute-intensive applications on Azure. It is particularly valuable for individuals preparing for the Microsoft Certified Azure Solutions Architect Expert certification or those working on large-scale cloud-native architectures. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/88/06d665359840b8a23e242b34570491/1024x1024.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:w8860NbcEeyrwhIbJtIqCQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Aw8860NbcEeyrwhIbJtIqCQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fyour-world-and-what-shapes-it&intsrc=APIG_9419 Your World and What Shapes It Thriving organizations strive for equity at all levels. Dynamic global DEI initiatives strengthen connectivity within individual teams and foster cross-cultural collaboration and mutual understanding while encouraging the inclusion of employees from all regions. Broader global relations aside, there also exist country-specific ethnic dynamics that make DEI a critical conversation. Global dynamics present a possible risk (and opportunity) for organizations. In this course, we will explore the complex topics that shape your views and ideas by exploring historical narratives while working on your journey towards improving cultural competence. This course can be taken for academic credit as part of CU Boulder’s Master of Engineering in Engineering Management (ME-EM) degree offered on the Coursera platform. The ME-EM is designed to help engineers, scientists, and technical professionals move into leadership and management roles in the engineering and technical sectors. With performance-based admissions and no application process, the ME-EM is ideal for individuals with a broad range of undergraduate education and/or professional experience. Learn more about the ME-EM program at https://www.coursera.org/degrees/me-engineering-management-boulder. Course logo image credit: Clay Banks. Available on Unsplash at https://unsplash.com/photos/LjqARJaJotc https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/06/58a8549b484971b3c48130706a91dd/clay-banks-LjqARJaJotc-unsplash.jpg?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:nqarRlIAEeeffgqJyG_Okg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AnqarRlIAEeeffgqJyG_Okg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ftechniques-of-design-oriented-analysis&intsrc=APIG_9419 Techniques of Design-Oriented Analysis This course can also be taken for academic credit as ECEA 5706, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is Course #2 in the Modeling and Control of Power Electronics course sequence. The course is focused on techniques of design-oriented analysis that allow you to quickly gain insights into models of switching power converters and to translate these insights into practical converter designs. The design-oriented techniques covered are the Extra Element Theorem and the N-Extra Element Theorem (N-EET). Through practical examples, it is shown how the EET can be used to simplify circuit analysis, to examine the effects of initially unmodeled components, and to design damping of converters such as SEPIC and Cuk to achieve high-performance closed-loop controls. The N-EET will allow you to perform circuit analysis and to derive circuit responses with minimum algebra. Modeling and design examples are supported by design-oriented MATLAB script and Spice simulations. After completion of this course, the student will gain analytical skills applicable to the design of high-performance closed-loop controlled switching power converters. We strongly recommend students complete the CU Boulder Power Electronics specialization as well as Course #1 Averaged-Switch Modeling and Simulation before enrolling in this course (the course numbers provided below are for students in the CU Boulder's MS-EE program): ● Introduction to Power Electronics (ECEA 5700) ● Converter Circuits (ECEA 5701) ● Converter Control (ECEA 5702) ● Averaged-Switch Modeling and Simulation (ECEA 5705) After completing this course, you will be able to: ● Understand statement and derivation of the Extra Element Theorem ● Apply the Extra Element Theorem to converter analysis and design problems ● Understand the statement of the N-Extra Element Theorem ● Apply the N-Extra Element Theorem to converter analysis and design problems ● Apply techniques of design-oriented analysis to analysis, design, and simulations of switching converters https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/24/476202e4504c30ada1983d9e811e41/ModelingControlPE_logo.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:tAfppJ3KEeWoKRLkmmHLTQ 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Pennsylvania Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AtAfppJ3KEeWoKRLkmmHLTQ&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fwharton-introduction-spreadsheets-models&intsrc=APIG_9419 Introduction to Spreadsheets and Models The simple spreadsheet is one of the most powerful data analysis tools that exists, and it’s available to almost anyone. Major corporations and small businesses alike use spreadsheet models to determine where key measures of their success are now, and where they are likely to be in the future. But in order to get the most out of a spreadsheet, you have the know-how to use it. This course is designed to give you an introduction to basic spreadsheet tools and formulas so that you can begin harness the power of spreadsheets to map the data you have now and to predict the data you may have in the future. Through short, easy-to-follow demonstrations, you’ll learn how to use Excel or Sheets so that you can begin to build models and decision trees in future courses in this Specialization. Basic familiarity with, and access to, Excel or Sheets is required. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/22/602700b00511e5ada4195d312ad6aa/Wharton_online_spreadsheets_8x8.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:A3ASpYtuEeiaTA5iU-pJGg 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AA3ASpYtuEeiaTA5iU-pJGg&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Findustrial-iot-project-planning-machine-learning&intsrc=APIG_9419 Project Planning and Machine Learning Products don't design and build themselves. In this course, students learn how to staff, plan and execute a project to build a product. We explore sensors, which produce tremendous volumes of data, and then storage devices and file systems for storing big data. Finally, we study machine learning and big data analytics. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Electrical Engineering (MS-EE) degree offered on the Coursera platform. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Electrical Engineering: https://www.coursera.org/degrees/msee-boulder https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/6b/acbd30b93811e89aaa7b5c488adb52/Logo-Image.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kl1SGpRzEembThIC62Cw7g 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software IBM Tue Jun 09 11:00:36 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3Akl1SGpRzEembThIC62Cw7g&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fibm-ai-workflow-business-priorities-data-ingestion&intsrc=APIG_9419 AI Workflow: Business Priorities and Data Ingestion This is the first course of a six part specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks. By the end of this course you should be able to: 1. Know the advantages of carrying out data science using a structured process 2. Describe how the stages of design thinking correspond to the AI enterprise workflow 3. Discuss several strategies used to prioritize business opportunities 4. Explain where data science and data engineering have the most overlap in the AI workflow 5. Explain the purpose of testing in data ingestion 6. Describe the use case for sparse matrices as a target destination for data ingestion 7. Know the initial steps that can be taken towards automation of data ingestion pipelines Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://coursera-course-photos.s3.amazonaws.com/91/01a8bbfcb5431bb1156e7e486e2def/IBM_AI_WORKFLOW.png?auto=format%2Ccompress&dpr=1&w=300&h=300&fit=crop 99.00 99.00 USD 15.00 45.00 InStock
crse:kWmOtoOVEeibxhKbPkCY-A 14726 Coursera B2C Affiliate Program 9419 coursera_products Software > Computer Software > Educational Software Educational Software University of Colorado Boulder Tue Jun 09 11:00:35 UTC 2026 https://imp.i384100.net/c/5661882/1242836/14726?prodsku=crse%3AkWmOtoOVEeibxhKbPkCY-A&u=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsensor-manufacturing-process-control&intsrc=APIG_9419 Sensor Manufacturing and Process Control Sensor Manufacturing and Process Control" can also be taken for academic credit as ECEA 5343, part of CU Boulder’s Master of Science in Electrical Engineering degree. This is our fourth course in our specialization on Embedding Sensor and Motors. To get the most out of this course, you should first take our first course entitled "Sensors and Sensor Circuits", our second course entitled "Motor and Motor Control Circuits", and our third course entitled "Pressure, Force, Motion, and Humidity Sensors". Our first course gives you a tutorial on how to use the hardware and software development kit we have chosen for the lab exercises. Our second and third courses give you three hands-on lab experiments using the kit. This third course assumes that you already know how to use the kit. You will learn about sensor signal characterization and manufacturing techniques and how to optimize the accuracy of sensors. You will also learn about more advanced sensors, proportional-integral-derivative (PID) control, and how this method is used to give you a closed loop sensor feedback system. After taking this course, you will be able to: ● Understand how sensor manufacturers characterize and calibrate their sensors. ● Tune a PID control loop and access the PID control function of the Cypress PSoC development kit for a motor control application. ● Understand manufacturing methods used to build electro-mechanical and micro-machined sensors. This course includes specific hardware and software requirements. Please review the FAQ below for complete details.
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Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
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Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Michigan
Capstone: Retrieving, Processing, and Visualizing Data with Python
In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
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Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
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Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
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COURSE
FREE TRIAL
Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
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COURSE
FREE TRIAL
Technology & Coding
Columbia University
Computational Methods in Pricing and Model Calibration
This course focuses on computational methods in option and interest rate, product’s pricing and model calibration. The first module will introduce different types of options in the market, followed by an in-depth discussion into numerical techniques helpful in pricing them, e.g. Fourier Transform (FT) and Fast Fourier Transform (FFT) methods. We will explain models like Black-Merton-Scholes (BMS), Heston, Variance Gamma (VG), which are central to understanding stock price evolution, through case studies and Python codes. The second module introduces concepts like bid-ask prices, implied volatility, and option surfaces, followed by a demonstration of model calibration for fitting market option prices using optimization routines like brute-force search, Nelder-Mead algorithm, and BFGS algorithm. The third module introduces interest rates and the financial products built around these instruments. We will bring in fundamental concepts like forward rates, spot rates, swap rates, and the term structure of interest rates, extending it further for creating, calibrating, and analyzing LIBOR and swap curves. We will also demonstrate the pricing of bonds, swaps, and other interest rate products through Python codes. The final module focuses on real-world model calibration techniques used by practitioners to estimate interest rate processes and derive prices of different financial products. We will illustrate several regression techniques used for interest rate model calibration and end the module by covering the Vasicek and CIR model for pricing fixed income instruments.
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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COURSE
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Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Cybersecurity for Data Science
This course aims to help anyone interested in data science understand the cybersecurity risks and the tools/techniques that can be used to mitigate those risks. We will cover the distinctions between confidentiality, integrity, and availability, introduce learners to relevant cybersecurity tools and techniques including cryptographic tools, software resources, and policies that will be essential to data science. We will explore key tools and techniques for authentication and access control so producers, curators, and users of data can help ensure the security and privacy of the data. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Pennsylvania
Data Analysis Using Python
This course provides an introduction to basic data science techniques using Python. Students are introduced to core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. This course provides an overview of loading, inspecting, and querying real-world data, and how to answer basic questions about that data. Students will gain skills in data aggregation and summarization, as well as basic data visualization.
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Technology & Coding
University of Colorado Boulder
Data Mining Methods
This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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COURSE
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Technology & Coding
University of Colorado Boulder
Data Mining Project
Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Data Science as a Field
This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the skills needed to be a data scientist; the process and pitfalls involved in data science; and the practice of data science in the professional and academic world. This course is part of CU Boulder’s Master’s of Science in Data Science and was collaboratively designed by both academics and industry professionals to provide learners with an insider’s perspective on this exciting, evolving, and increasingly vital discipline. Data Science as a Field can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Ethical Issues in Data Science
Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning. This course examines some of the ethical issues related to data science, with the fundamental objective of making data science professionals aware of and sensitive to ethical considerations that may arise in their careers. It does this through a combination of discussion of ethical frameworks, examination of a variety of data science applications that lead to ethical considerations, reading current media and scholarly articles, and drawing upon the perspectives and experiences of fellow students and computing professionals. Ethical Issues in Data Science can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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COURSE
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Colorado Boulder
Fundamentals of Data Visualization
Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of such information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include user-centered design, web-based visualization, data cognition and perception, and design evaluation. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
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Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
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Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
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Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
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Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
New York Institute of Finance
Future Development in Supply Chain Finance and Blockchain Technology
This course focuses on future developments in Supply Chain Finance such as Artificial Intelligence (AI) and Application Programming Interfaces (APIs). This course covers the basic concepts of Distributed Ledger Technology (DLT), the key features as well as the benefits the solution represents for Supply Chain Finance. This course concludes with an understanding of the applicability of Blockchain Technologies and how they impact Supply Chain Finance.
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Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
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COURSE
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Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of California San Diego
Genome Assembly Programming Challenge
In Spring 2011, thousands of people in Germany were hospitalized with a deadly disease that started as food poisoning with bloody diarrhea and often led to kidney failure. It was the beginning of the deadliest outbreak in recent history, caused by a mysterious bacterial strain that we will refer to as E. coli X. Soon, German officials linked the outbreak to a restaurant in Lübeck, where nearly 20% of the patrons had developed bloody diarrhea in a single week. At this point, biologists knew that they were facing a previously unknown pathogen and that traditional methods would not suffice – computational biologists would be needed to assemble and analyze the genome of the newly emerged pathogen. To investigate the evolutionary origin and pathogenic potential of the outbreak strain, researchers started a crowdsourced research program. They released bacterial DNA sequencing data from one of a patient, which elicited a burst of analyses carried out by computational biologists on four continents. They even used GitHub for the project: https://github.com/ehec-outbreak-crowdsourced/BGI-data-analysis/wiki The 2011 German outbreak represented an early example of epidemiologists collaborating with computational biologists to stop an outbreak. In this online course you will follow in the footsteps of the bioinformaticians investigating the outbreak by developing a program to assemble the genome of the E. coli X from millions of overlapping substrings of the E.coli X genome.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Pennsylvania
Introduction to Java and Object-Oriented Programming
This course provides an introduction to the Java language and object-oriented programming, including an overview of Java syntax and how it differs from a language like Python. Students will learn how to write custom Java classes and methods, and how to test their code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Toronto
Introduction to Self-Driving Cars
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
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Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
Alberta Machine Intelligence Institute
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
New York Institute of Finance
Operational Risk Management: Frameworks & Strategies
In the final course from the Risk Management specialization, you will be introduced to the different roles in risk governance and the benefits of establishing an operational risk management program at your own workplace. This course will highlight key elements of an Operational Risk Management framework and help you identify the appropriate elements to incorporate in your own program. By the end of the course, you will be able to capture, report, and investigate operational risk events, produce meaningful key risk indicator (KRI) data and trend analysis, assess organizational risk appetite, and design an Operational Risk Control and Self-Assessment program. To be successful in this course, you should have a basic knowledge of statistics and probability and familiarity with business operations. Experience with MS Excel and Python recommended.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
SAS
Practical SAS Programming and Certification Review
In this course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Prediction and Control with Function Approximation
In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
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Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
FREE TRIAL
Technology & Coding
Alberta Machine Intelligence Institute & University of Alberta
Sample-based Learning Methods
In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Colorado Boulder
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
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Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois at Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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COURSE
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Accounting Data Analytics with Python
This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data.
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
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Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
BoostLyke may earn a commission when you enroll via this link. Affiliate disclosure.
Sign Up & Start Your FREE Trial
COURSE
FREE TRIAL
Technology & Coding
University of Illinois Urbana-Champaign
Introduction to Accounting Data Analytics and Visualization
Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE).
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