Earn Coins on World-Class Courses

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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+.

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.

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.

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

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.

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.

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.

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.

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.

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.

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".

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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

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.

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.

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

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.

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!

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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

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.