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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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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).

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

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

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.

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.

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.

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.

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).

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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

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

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.

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.