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• Social Sciences

CS50: Introduction to Computer Science

An introduction to the intellectual enterprises of computer science and the art of programming.

CS50's Introduction to Artificial Intelligence with Python

Learn to use machine learning in Python in this introductory course on artificial intelligence.

CS50's Understanding Technology

This is CS50’s introduction to technology for students who don’t (yet!) consider themselves computer persons.

CS50's Introduction to Programming with Scratch

A gentle introduction to programming that prepares you for subsequent courses in coding.

CS50's Computer Science for Business Professionals

This is CS50’s introduction to computer science for business professionals.

CS50's Web Programming with Python and JavaScript

This course picks up where CS50 leaves off, diving more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.

CS50 for Lawyers

This course is a variant of Harvard University's introduction to computer science, CS50, designed especially for lawyers (and law students).

Using Python for Research

Take your introductory knowledge of Python programming to the next level and learn how to use Python 3 for your research.

Quantitative Methods for Biology

Learn introductory programming and data analysis in MATLAB, with applications to biology and medicine.

MLOps for Scaling TinyML

This course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning). Learners explore best practices to deploy, monitor, and maintain (tiny) Machine Learning models in production at scale.

860+ Free Online Programming & Computer Science Courses You Can Start This New Year

Twelve years ago, universities like Stanford and MIT opened up free online courses to the public. Today, over 1,200 schools around the world have created thousands of free online courses.

To welcome the new year, I’ve compiled this list of 860+ such free online courses that you can start right now. For this, I leveraged Class Central ’s database of over 100,000 online courses . When available, I've also included the course average rating.

I’ve sorted these courses into the following categories, based on their difficulty level:

• Intermediate

Courses that are being offered for the first time are marked as [NEW] . Almost all of these courses are completely self-paced, meaning you can start right now.

You can find complete lists of technology-related courses on Class Central’s Computer Science , Data Science , and Programming subject pages. If the options feels overwhelming, there, you'll also find guides to help you find the best course for you.

Finally, if you have trouble figuring out how to sign up for Coursera courses for free, don’t worry — I’ve written an article on how to do that, too .

Intermediate (457)

• Using Databases with Python from University of Michigan ★★★★★(4060)
• R Programming from Johns Hopkins University ★★★☆☆(246)
• The Data Scientist’s Toolbox from Johns Hopkins University ★★★☆☆(166)
• Machine Learning for Musicians and Artists from Goldsmiths University of London ★★★★★(89)
• Divide and Conquer, Sorting and Searching, and Randomized Algorithms from Stanford University ★★★★★(68)
• Functional Programming Principles in Scala from École Polytechnique Fédérale de Lausanne ★★★★★(66)
• Data Science and Agile Systems for Product Management from University System of Maryland ★★★★★(62)
• Algorithms, Part I from Princeton University ★★★★★(59)
• Getting and Cleaning Data from Johns Hopkins University ★★★☆☆(57)
• Cryptography I from Stanford University ★★★★★(53)
• Intelligenza Artificiale from University of Urbino ★★★★★(49)
• Python for Data Science from University of California, San Diego ★★★★☆(47)
• Introduction to Data Science in Python from University of Michigan ★★☆☆☆(46)
• Object-Oriented Design from University of Alberta ★★★★☆(39)
• Introduction to Big Data from University of California, San Diego ★★★☆☆(35)
• Making Sense of Data in the Media from The University of Sheffield ★★★★★(35)
• Statistical Inference from Johns Hopkins University ★★★☆☆(34)
• Regression Models from Johns Hopkins University ★★★☆☆(34)
• Probability and Statistics in Data Science using Python from University of California, San Diego ★★☆☆☆(31)
• CS188.1x: Artificial Intelligence from University of California, Berkeley ★★★★★(30)
• Principles of Computing (Part 1) from Rice University ★★★★★(30)
• Reproducible Research from Johns Hopkins University ★★★★☆(27)
• Practical Machine Learning from Johns Hopkins University ★★★☆☆(27)
• Programming Languages, Part A from University of Washington ★★★★★(27)
• Responsive Website Basics: Code with HTML, CSS, and JavaScript from University of London International Programmes ★★★★☆(27)
• Mining Massive Datasets from Stanford University ★★★★★(25)
• A Crash Course in Data Science from Johns Hopkins University ★★★★☆(25)
• Design Patterns from University of Alberta ★★★★★(25)
• Software Security from University of Maryland, College Park ★★★★☆(24)
• Algorithmic Toolbox from University of California, San Diego ★★★★☆(23)
• Introducción a la Inteligencia Artificial: Principales Algoritmos from Galileo University ★★★★★(22)
• Algorithms, Part II from Princeton University ★★★★★(21)
• Cloud Computing Concepts, Part 1 from University of Illinois at Urbana-Champaign ★★★☆☆(21)
• Data Visualization from University of Illinois at Urbana-Champaign ★★★☆☆(21)
• Learning From Data (Introductory Machine Learning) from California Institute of Technology ★★★★★(21)
• Statistics and R from Harvard University ★★★★☆(20)
• Automata Theory from Stanford University ★★★★☆(20)
• Introduction to Machine Learning Course from Stanford University ★★★★☆(20)
• Data Analysis with R from Facebook ★★★★★(18)
• MongoDB for Java Developers
• Process Mining: Data science in Action from Eindhoven University of Technology ★★★★☆(17)
• Data Structures from University of California, San Diego ★★★★☆(16)
• Principles of Computing (Part 2) from Rice University ★★★★☆(16)
• Software Architecture from University of Alberta ★★★★☆(16)
• How to Code: Simple Data from The University of British Columbia ★★★★☆(15)
• Algorithmic Thinking (Part 1) from Rice University ★★★★☆(15)
• The Nature of Code from Processing Foundation ★★★★★(15)
• Design of Computer Programs from Stanford University ★★★★☆(14)
• Python for Genomic Data Science from Johns Hopkins University ★★★☆☆(14)
• Building a Data Science Team from Johns Hopkins University ★★★★☆(14)
• Text Retrieval and Search Engines from University of Illinois at Urbana-Champaign ★★★☆☆(14)
• Responsive Web Design from University of London International Programmes ★★★★☆(14)
• Data Analysis: Take It to the MAX() from Delft University of Technology ★★★☆☆(13)
• Intro to Data Science
• Using Python for Research from Harvard University ★★★★☆(12)
• Data Science in Real Life from Johns Hopkins University ★★★☆☆(12)
• Data Science Math Skills from Duke University ★★★★☆(12)
• Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues from Georgia Institute of Technology ★★★★★(12)
• Discrete Optimization from University of Melbourne ★★★★☆(12)
• Introduction to Software Product Management from University of Alberta ★★★★☆(12)
• Data Science: R Basics from Harvard University ★★★★★(11)
• Interactivity with JavaScript from University of Michigan ★★★★☆(11)
• Human-Computer Interaction I: Fundamentals & Design Principles from Georgia Institute of Technology ★★★★★(11)
• Biology Meets Programming: Bioinformatics for Beginners from University of California, San Diego ★★★☆☆(10)
• Programming Languages from University of Virginia ★★★☆☆(10)
• Learning from Data (Introductory Machine Learning course) from California Institute of Technology ★★★★★(10)
• Full Stack Foundations
• Managing Data Analysis from Johns Hopkins University ★★★☆☆(9)
• Data Visualization and Communication with Tableau from Duke University ★★★★☆(9)
• Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure from University of Illinois at Urbana-Champaign ★★★☆☆(9)
• Algorithmic Thinking (Part 2) from Rice University ★★★★☆(9)
• Data Wrangling with MongoDB from MongoDB University ★★★☆☆(9)
• Advanced Styling with Responsive Design from University of Michigan ★★★★☆(8)
• Machine Learning Fundamentals from University of California, San Diego ★★★★☆(8)
• Foundations of Data Analysis - Part 1: Statistics Using R from The University of Texas at Austin ★★★★☆(8)
• Cryptography from University of Maryland, College Park ★★★★☆(8)
• MATLAB and Octave for Beginners from École Polytechnique Fédérale de Lausanne ★★★★☆(8)
• Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital from Duke University ★★★★☆(7)
• Guided Tour of Machine Learning in Finance from New York University (NYU) ★☆☆☆☆(7)
• Software Testing from University of Utah ★★★★☆(7)
• Intro to DevOps from Nutanix ★★★☆☆(7)
• Computer Graphics from University of California, San Diego ★★★★☆(6)
• Data Structures and Performance from University of California, San Diego ★★★★☆(6)
• Internet of Things: How did we get here? from University of California, San Diego ★★☆☆☆(6)
• Computer Architecture from Princeton University ★★★★☆(6)
• Analysis of Algorithms from Princeton University ★★★★☆(6)
• Managing Big Data with MySQL from Duke University ★★★★☆(6)
• Computer Networking from Georgia Institute of Technology ★★★★☆(6)
• Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps from Georgia Institute of Technology ★★★★★(6)
• Cloud Computing Concepts: Part 2 from University of Illinois at Urbana-Champaign ★★★★★(6)
• Web Application Development with JavaScript and MongoDB from University of London International Programmes ★★★★☆(6)
• Introduction to Meteor.js Development from University of London International Programmes ★★★★☆(6)
• Client Needs and Software Requirements from University of Alberta ★★★★★(6)
• Interactive Computer Graphics from University of Tokyo ★★★☆☆(6)
• Fundamentals of Machine Learning from Santa Fe Institute ★★★★★(6)
• Software Debugging from Saarland University ★★★★★(6)
• Intro to Algorithms
• Intro to AJAX
• Intro to Data Analysis
• Networking for Web Developers
• Software Construction in Java from Massachusetts Institute of Technology ★★★★★(5)
• Internet of Things: Setting Up Your DragonBoard™ Development Platform from University of California, San Diego ★★★☆☆(5)
• Data Structures: An Active Learning Approach from University of California, San Diego ★★★★★(5)
• Software Development Process from Georgia Institute of Technology ★★★★☆(5)
• Cloud Networking from University of Illinois at Urbana-Champaign ★★★★☆(5)
• Social Network Analysis from University of California, Davis ★★★★★(5)
• Database Management Essentials from University of Colorado System ★★★★☆(5)
• Data Management and Visualization from Wesleyan University ★★☆☆☆(5)
• JavaScript Promises from Google ★★★★★(5)
• Data Science Essentials from Microsoft ★★★★☆(5)
• Programming for Everyone – An Introduction to Visual Programming Languages from Weizmann Institute of Science ★★★★☆(5)
• Parallel Programming Concepts
• Data Visualization and D3.js
• Algorithms on Strings from University of California, San Diego ★★★☆☆(4)
• The R Programming Environment from Johns Hopkins University ★★★☆☆(4)
• Data Manipulation at Scale: Systems and Algorithms from University of Washington ★★★☆☆(4)
• Programming Languages, Part B from University of Washington ★★★★☆(4)
• Data Analysis Tools from Wesleyan University ★★★☆☆(4)
• Querying Data with Transact-SQL from Microsoft ★★★★☆(4)
• Programming with Python for Data Science from Microsoft ★★★★☆(4)
• Practical Numerical Methods with Python from George Washington University ★★★★☆(4)
• Algorithms on Graphs from University of California, San Diego ★★★★☆(3)
• Internet of Things: Communication Technologies from University of California, San Diego ★★★☆☆(3)
• Mastering the Software Engineering Interview from University of California, San Diego ★★★★☆(3)
• Big Data Modeling and Management Systems from University of California, San Diego ★★★☆☆(3)
• Big Data Integration and Processing from University of California, San Diego ★★★★☆(3)
• Algorithmic Design and Techniques from University of California, San Diego ★★★★☆(3)
• Machine Learning: Unsupervised Learning from Brown University ★★★☆☆(3)
• Software Architecture & Design from Georgia Institute of Technology ★★★★★(3)
• Human-Computer Interaction II: Cognition, Context & Culture from Georgia Institute of Technology ★★★★★(3)
• Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms from Georgia Institute of Technology ★★★★★(3)
• Framework for Data Collection and Analysis from University of Maryland, College Park ★★★★☆(3)
• Data Warehouse Concepts, Design, and Data Integration from University of Colorado System ★★★☆☆(3)
• Service-Oriented Architecture from University of Alberta ★★★★☆(3)
• Website Performance Optimization from Google ★★★★☆(3)
• DevOps for Developers: How to Get Started from Microsoft ★★★★☆(3)
• Build a Modern Computer from First Principles: Nand to Tetris Part II (project-centered course) from Hebrew University of Jerusalem ★★★★★(3)
• Data Mining with Weka from University of Waikato ★★★★☆(3)
• MongoDB for .NET Developers
• Configuring Linux Web Servers
• Language, Proof and Logic from Stanford University ★★★★★(2)
• Mobile Application Experiences Part 1: From a Domain to an App Idea from Massachusetts Institute of Technology ★★★★★(2)
• Software Development Fundamentals from University of Pennsylvania ★★★☆☆(2)
• Building R Packages from Johns Hopkins University ★★★☆☆(2)
• Data Science: Computational Thinking with Python from University of California, Berkeley ★★★★★(2)
• Advanced Data Structures in Java from University of California, San Diego ★★★★☆(2)
• Introduction to Machine Learning from Duke University ★★★★☆(2)
• Human-Computer Interaction IV: Evaluation, Agile Methods & Beyond from Georgia Institute of Technology ★★★★★(2)
• Programming Languages, Part C from University of Washington ★★★★☆(2)
• Data Analytics Foundations for Accountancy I from University of Illinois at Urbana-Champaign ★☆☆☆☆(2)
• Object-Oriented Data Structures in C++ from University of Illinois at Urbana-Champaign ★★★★☆(2)
• Responsive Website Tutorial and Examples from University of London International Programmes ★★★★★(2)
• How to Code: Complex Data from The University of British Columbia ★★★★★(2)
• Fundamentals of Visualization with Tableau from University of California, Davis ★★★★☆(2)
• Algorithms from Indian Institute of Technology Bombay ★★★★★(2)
• Software Testing Management from University System of Maryland ★★☆☆☆(2)
• Web Application Development: Basic Concepts from University of New Mexico ★★★★★(2)
• Client-Server Communication from Google ★★★★★(2)
• Introduction to Computation Theory from Santa Fe Institute ★★★★★(2)
• Developing International Software, Part 1 from Microsoft ★★★★☆(2)
• Principles of Machine Learning from Microsoft ★★★★★(2)
• Data, Analytics, and Learning from University of Texas Arlington ★★☆☆☆(2)
• Fundamentals of Machine Learning in Finance from New York University (NYU) ★★☆☆☆(2)
• Autonomous Mobile Robots from ETH Zurich ★★★★★(2)
• Knowledge Management and Big Data in Business from Hong Kong Polytechnic University ★★★★☆(2)
• Approximation Algorithms Part I from École normale supérieure ★★★★★(2)
• Introduction to Process Mining with ProM from Eindhoven University of Technology ★★★★☆(2)
• HTML5 Apps and Games from World Wide Web Consortium (W3C) ★★☆☆☆(2)
• Intro to Theoretical Computer Science
• Compilers from Stanford University ★★★★★(1)
• Graph Search, Shortest Paths, and Data Structures from Stanford University ★☆☆☆☆(1)
• Computation Structures 2: Computer Architecture from Massachusetts Institute of Technology ★★★★☆(1)
• Computational Thinking for Modeling and Simulation from Massachusetts Institute of Technology ★★☆☆☆(1)
• Advanced Algorithms and Complexity from University of California, San Diego ★★★☆☆(1)
• Advanced R Programming from Johns Hopkins University ★★★★☆(1)
• Advanced Linear Models for Data Science 2: Statistical Linear Models from Johns Hopkins University ★★★★★(1)
• Foundations of Healthcare Systems Engineering from Johns Hopkins University ★★★★★(1)
• Programming for the Internet of Things Project from University of California, Irvine ★★★★☆(1)
• Intro to Analytic Thinking, Data Science, and Data Mining from University of California, Irvine ★★★★★(1)
• Basic Data Processing and Visualization from University of California, San Diego ★★★☆☆(1)
• Database Systems Concepts & Design from Georgia Institute of Technology ★★★★☆(1)
• Human-Computer Interaction III: Ethics, Needfinding & Prototyping from Georgia Institute of Technology ★★★★★(1)
• Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms from Georgia Institute of Technology ★★★★★(1)
• Blockchain Basics from University at Buffalo ★★☆☆☆(1)
• Mathematics for Computer Science from University of London International Programmes ★★★★★(1)
• Software Construction: Data Abstraction from The University of British Columbia ★★★☆☆(1)
• Software Construction: Object-Oriented Design from The University of British Columbia ★★☆☆☆(1)
• DevOps Culture and Mindset from University of California, Davis ★★★★★(1)
• Software Development Processes and Methodologies from University of Minnesota ★★★☆☆(1)
• Server-side Development with NodeJS, Express and MongoDB from The Hong Kong University of Science and Technology ★★★★★(1)
• Essential Linear Algebra for Data Science from University of Colorado Boulder ★★★★☆(1)
• Data Analysis: Visualization and Dashboard Design from Delft University of Technology ★★★☆☆(1)
• Automated Software Testing: Unit Testing, Coverage Criteria and Design for Testability from Delft University of Technology ★★★★☆(1)
• Automated Software Testing: Model and State-based Testing from Delft University of Technology ★★★★★(1)
• Fundamentals of Network Communication from University of Colorado System ★★★★★(1)
• Linux Server Management and Security from University of Colorado System ★★★☆☆(1)
• Hacking and Patching from University of Colorado System ★★★★★(1)
• Formal Software Verification from University System of Maryland ★★☆☆☆(1)
• Software Architecture for the Internet of Things from EIT Digital ★★★★☆(1)
• Paradigms of Computer Programming from Université catholique de Louvain ★★★★☆(1)
• Client-based Web Applications development: ReactJS & Angular from Universidad Politécnica de Madrid ★☆☆☆☆(1)
• Advanced Algorithmics and Graph Theory with Python from Institut Mines-Télécom ★★★★★(1)
• Browser Rendering Optimization from Google ★★★★☆(1)
• Google Maps APIs from Google ★★★★★(1)
• Cybersecurity and Mobility from University System of Georgia ★☆☆☆☆(1)
• Developing SQL Databases from Microsoft ★★☆☆☆(1)
• Introduction to TypeScript 2 from Microsoft ★☆☆☆☆(1)
• Building Interactive Prototypes using JavaScript from Microsoft ★★★★☆(1)
• Introduction to C# from Microsoft ★☆☆☆☆(1)
• Algorithms and Data Structures from Microsoft ★★☆☆☆(1)
• Algorithms and Data Structures in C# from Microsoft ★★★★★(1)
• 2D Game Development with libGDX from Amazon ★★★★★(1)
• Overview of Advanced Methods of Reinforcement Learning in Finance from New York University (NYU) ★☆☆☆☆(1)
• Advanced Database Queries from New York University (NYU) ★★★★★(1)
• Advanced Database Administration from New York University (NYU) ★★★★★(1)
• Introduction to Cloud Infrastructure Technologies from Linux Foundation ★★★★☆(1)
• 用Python玩转数据 Data Processing Using Python from Nanjing University ★★★★★(1)
• Data Processing Using Python from Nanjing University ★★★☆☆(1)
• Fundamentals of Parallelism on Intel Architecture from Intel ★★★★★(1)
• An Introduction to Practical Deep Learning from Intel ★★★☆☆(1)
• How to Win Coding Competitions: Secrets of Champions from ITMO University ★★★☆☆(1)
• Hacker101 from HackerOne ★★★★★(1)
• Diseño de Sistemas de información gerencial para Internet con MySQL / PHP y Joomla from Universidad del Rosario ★★★★★(1)
• Technical Interview from Pramp ★★★★★(1)
• Designing RESTful APIs
• MongoDB for Javascript Developers
• Quantitative Methods for Biology from Harvard University
• Fundamentals of TinyML from Harvard University
• Probabilistic Graphical Models 3: Learning from Stanford University
• Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming from Stanford University
• Shortest Paths Revisited, NP-Complete Problems and What To Do About Them from Stanford University
• Algorithms: Design and Analysis, Part 1 from Stanford University
• Algorithms: Design and Analysis, Part 2 from Stanford University
• Mobile Application Experiences Part 3: Building Mobile Apps from Massachusetts Institute of Technology
• Advanced Software Construction in Java from Massachusetts Institute of Technology
• Mobile Application Experiences from Massachusetts Institute of Technology
• Machine Learning for Healthcare from Massachusetts Institute of Technology
• Data Structures and Software Design from University of Pennsylvania
• Algorithm Design and Analysis from University of Pennsylvania
• Data Analysis Using Python from University of Pennsylvania
• Building Web Applications in Django from University of Michigan
• Developing AR/VR/MR/XR Apps with WebXR, Unity & Unreal from University of Michigan
• Intro to AR/VR/MR/XR: Technologies, Applications & Issues from University of Michigan
• Introduction to Machine Learning in Sports Analytics from University of Michigan
• Introduction to Neurohacking In R from Johns Hopkins University
• Building Data Visualization Tools from Johns Hopkins University
• Exploratory Data Analysis from Johns Hopkins University
• The Merkle Tree and Cryptocurrencies from University of California, Irvine
• Data Science: Machine Learning and Predictions from University of California, Berkeley
• Blockchain Technology from University of California, Berkeley
• Data, Models and Decisions in Business Analytics from Columbia University
• Internet of Things: Sensing and Actuation From Devices from University of California, San Diego
• Minecraft, Coding and Teaching from University of California, San Diego
• How Virtual Reality Works from University of California, San Diego
• Introduction to Genomic Data Science from University of California, San Diego
• Data Structures Fundamentals from University of California, San Diego
• Graph Algorithms from University of California, San Diego
• String Processing and Pattern Matching Algorithms from University of California, San Diego
• Computer Science: Algorithms, Theory, and Machines from Princeton University
• Java Programming: Build a Recommendation System from Duke University
• Cloud Data Engineering from Duke University
• Cloud Virtualization, Containers and APIs from Duke University
• Machine Learning Foundations for Product Managers from Duke University
• Managing Machine Learning Projects from Duke University
• Web Applications and Command-Line Tools for Data Engineering from Duke University
• Human-Computer Interaction from Georgia Institute of Technology
• Software Analysis & Testing from Georgia Institute of Technology
• Introduction to Graduate Algorithms from Georgia Institute of Technology
• Network Function Virtualization from Georgia Institute of Technology
• Cloud Systems Software from Georgia Institute of Technology
• Cloud Applications from Georgia Institute of Technology
• Cybersecurity: The CISO's View from University of Washington
• Building a Cybersecurity Toolkit from University of Washington
• Finding Your Cybersecurity Career Path from University of Washington
• Machine Teaching for Autonomous AI from University of Washington
• Designing Autonomous AI from University of Washington
• Building Autonomous AI from University of Washington
• Data Analytics Foundations for Accountancy II from University of Illinois at Urbana-Champaign
• Data Modeling and Regression Analysis in Business from University of Illinois at Urbana-Champaign
• Unordered Data Structures from University of Illinois at Urbana-Champaign
• Ordered Data Structures from University of Illinois at Urbana-Champaign
• Machine Learning for Accounting with Python from University of Illinois at Urbana-Champaign
• Machine Learning Algorithms with R in Business Analytics from University of Illinois at Urbana-Champaign
• Advanced Deep Learning Methods for Healthcare from University of Illinois at Urbana-Champaign
• Blockchain Platforms from University at Buffalo
• Smart Contracts from University at Buffalo
• Decentralized Applications (Dapps) from University at Buffalo
• Data Analysis and Visualization from University at Buffalo
• Supercomputing from Partnership for Advanced Computing in Europe
• Introduction to Predictive Analytics using Python from University of Edinburgh
• Foundations of Data Science: K-Means Clustering in Python from University of London International Programmes
• Introduction to Applied Cryptography from University of London International Programmes
• Implementation Strategies: AI and Cognitive Computing from The University of British Columbia
• Implementation Strategies: Cloud Computing from The University of British Columbia
• Worldbuilding for Video Games from The University of British Columbia
• Creating Dashboards and Storytelling with Tableau from University of California, Davis
• Essential Design Principles for Tableau from University of California, Davis
• Continuous Integration from University of California, Davis
• Data Wrangling, Analysis and AB Testing with SQL from University of California, Davis
• Identifying Security Vulnerabilities in C/C++Programming from University of California, Davis
• Exploiting and Securing Vulnerabilities in Java Applications from University of California, Davis
• Distributed Computing with Spark SQL from University of California, Davis
• Game Design and Development 3: 3D Shooter from Michigan State University
• Game Design and Development 2: 2D Platformer from Michigan State University
• Game Design and Development 1: 2D Shooter from Michigan State University
• Game Design and Development 4: 3D Platformer from Michigan State University
• Moving to the Cloud from University of Melbourne
• Signals, Systems, and Learning from Rice University
• Bioinformatics: Introduction and Methods 生物信息学: 导论与方法 from Peking University
• 面向对象技术高级课程（The Advanced Object-Oriented Technology） from Peking University
• 操作系统与虚拟化安全 from Peking University
• 计算机组成 Computer Organization from Peking University
• 计算机辅助翻译原理与实践 Principles and Practice of Computer-Aided Translation from Peking University
• 数据结构基础 from Peking University
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More From Forbes

Google introduces new ai training course.

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Google is introducing two training opportunities to increase basic AI skills for the workforce

Google has announced a new major training initiative designed to increase AI skills throughout the workforce.

The project includes a new Google Google Google AI Essentials Course that will be offered on Coursera Coursera , the online learning platform, in addition to a $75 million AI Opportunity Fund to make the course more widely available.

The company’s new online Google AI Essentials course is designed and taught by Google experts in AI. The course, which will not require a prior degree or AI experience, will focus on “teaching workers foundational AI skills, AI best practices, and how to use AI responsibly.” Google claims that “in under 10 hours of self-paced study,” students will gain an introduction to AI, learn how to write effective prompts and be able to identify AI’s potential biases and harm.

Designed to give learners hands-on experience using AI in their work, the course, which will cost $49 on Coursera, will involve videos, readings, and interactive exercises. Individuals who complete the course will earn a certificate from Google.

The $75 million Google AI Opportunity Fund , offered through the company’s philanthropic arm, is aimed at enabling at least one million Americans take the course and learn basic generative AI skills by providing grants to workforce development and education organizations.

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For example, Miami Dade College will offer the course to all students enrolled in its AI college-degree program, and the University of Virginia’s Darden Executive Education and Lifelong Learning will provide the course to working learners.

These and other organizations will allow the AI training to be extended for free to several segments of society, including rural and underserved communities, educators and students, public sector workers, nonprofit leaders and small businesses.

“AI offers significant opportunities to accelerate economic growth, particularly if people have access to the right resources and training,” said James Manyika, senior vice president for research, technology & society at Google, in a release shared with the press.

“Google.org’s new AI Opportunity Fund and Google’s AI Essentials Course are important next steps in our commitment to ensure everyone, everywhere can access AI training. No single employer or policymaker will be able to modernize workforce programs on their own – we are committed to collaborating across industry, civil society and government to ensure the opportunities created by new technologies are available to everyone,” Manyika added.

Despite the increasing demand for AI skills across several industry sectors, the World Economic Forum estimates that only half of workers have access to adequate AI training today. To equip as many workers as possible with foundational AI skills, Google introduced an AI Opportunity Agenda last year.

One of the first recipients of Google.org’s AI Opportunity Fund grant will be Goodwill. With more than 80% of Americans living within 10 miles of a Goodwill, it’s expected the organization will be able to offer Google’s AI Essentials course at scale to many local communities at no cost.

“Beginning in 2017, our digital skills training work with Google.org has unlocked new opportunities for hundreds of thousands of workers, including many in lower-wage jobs,” said Steve Preston, president and CEO of Goodwill Industries International, in the news release. “By expanding our efforts and leveraging Google’s new AI Essentials course, we can help US jobseekers attain the core digital and AI skills needed to step into well-paying jobs and greater prospects for economic mobility.”

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Launching in Fall 2024, the Hamel Honors and Scholars College will provide a community for academically motivated students who want to take their college education to a higher level.

Honors Community

Welcoming all majors and offering options for personalized studies, the Honors College is home to a diverse group of high-achieving students. We strive to create an environment where our students feel connected to one another in Honors as well as to the entire campus and local community.

Experiential Learning

In the Honors College, we know that learning happens as much outside the classroom as inside it. UNH offers hundreds of high-impact learning experiences that count as Honors Units. The more time-consuming an experience is, the more Honors Units it earns.  

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The Honors College has a wide variety of courses that allow students to challenge themselves. Go deep into special topics with great professors in active, stimulating Honors Discovery courses, seminars, and symposia.

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You choose what experiences will complete your Honors College portfolio. Combine courses with research, travel, community work, and hands-on learning to shape your own path.

32 UNITS = GRADUATE WITH UNIVERSITY HONORS

Overview of honors curriculum.

The Honors College provides a community for motivated, inquisitive, high achieving students to interact with outstanding faculty while engaging in experiential and community-based learning, external engagement, and meaningful scholarship. In order to honor a commitment to true curricular flexibility and innovation, the Honors College will implement a "unit-based" rather than a "credit-based" system for keeping track of students' progress.

Honors Units can be earned from both classroom and non-classroom experiences. The Honors Units are calculated based on credit hours for courses and credit-hour-equivalent time requirements for non-classroom experiences.

What are the requirements that go into the 32 Honors Units?

Every Honors student must complete 32 Units to graduate with University Honors. While you need to have some units in each category, the distribution is up to you! For example, you can take as few as 2 Honors courses, but if you enjoy our discussion-based seminars, there's no limit on how many you can take. If you prefer a more hands-on approach, you can complete most of your Honors Units via cocurricular experiences like research, study abroad, and project-based learning. An Honors Advisor can help you plan your personal Honors curriculum.

Possible Tracks to Honors Graduation

The flexibility of the Honors College curriculum provides students with an abundance of choices to complete their undergraduate career in a way that favors their special interests. Below are examples of ways Honors students may consider structuring their Honors experience to complete 32 Honors units that most appeals to them academically, socially, and personally.

Disciplinary Depth Track

An Honors student who wishes to use their Honors thesis as their Culminating Experience may choose the Disciplinary Depth track. They would earn their 32 Honors Units mostly through courses and Honors thesis, spending less time on Gateway and Cocurricular Experiences.

Synthesis Track

An Honors student who likes looking at the "Big Picture" and enjoys project-based learning may find success in the Synthesis track. This track is designed to take an idea or a statement and design one's Honors experience around its study.

For example, at end of their sophomore year a student brings their synthesis plan to an Honors Advisor: fighting the bias of "girls can’t do math."

The student and advisor agree on a synthesis deliverable. In this student's case, the deliverable will be a proposal for a children’s book about a Hispanic girl overcoming the bias of others’ expectations to win a math contest. This deliverable is a foundation for the rest of the student's studies. They may write a  reflection paper of how they discovered the need for their book through their Honors seminar course, discipline courses, and community volunteering efforts; and how their experiences came together in a greater understanding of the issues of bias and how to effectively address them.

Integrated Track

Some departments choose to specify a different Culminating Experience in their Honors in Major program. For example, department faculty might decide that an Honors version of a group design project might be more valuable than an individual research project.

• E-center hackathon
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• Developing a prototype design for a wearable sensor that can  assess associations between risk factors and falls in the elderly  that is informed by ethical, human-centered design principles.

Any thesis or final project that has been successfully completed by a student under the applicable requirements for Honors in Major shall also be accepted as the Culminating Experience for earning University Honors. Honors Units will correspond to credits.

Parallel Track

Instead of a project, a student may parallel their studies with a relevant internship or fellowship. For example, a student who is interested in Sustainability may choose to take Sustainability-focused courses paired with a Sustainability Fellowship .  Their final Honors in Major project may be a presentation on how their course work guided them in their fellowship and how they might put what they learned during their fellowship experience towards a career in Sustainability.

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Canada to introduce new rules around off-campus work hours for international students

From: Immigration, Refugees and Citizenship Canada

News release

International students enrich Canada’s social, cultural and economic fabric. That is why, in recent months, Immigration, Refugees and Citizenship Canada has introduced reforms to the International Students Program, to ensure system integrity while protecting students from fraud and financial vulnerability.

April 29, 2024—Ottawa— International students enrich Canada’s social, cultural and economic fabric. That is why, in recent months, Immigration, Refugees and Citizenship Canada has introduced reforms to the International Student Program, to ensure system integrity while protecting students from fraud and financial vulnerability.

The Honourable Marc Miller, Minister of Immigration, Refugees and Citizenship, announced today that the temporary policy allowing students to work more than 20 hours per week off campus will come to an end on April 30, 2024, and it will not be extended. This fall, we intend to change the number of hours students may work off campus per week to 24 hours.

Students who come to Canada must be here to study. As such, allowing students to work up to 24 hours per week will ensure they focus primarily on their studies, while having the option to work, if necessary.

As we head into the summer session, students who have a scheduled academic break can continue working unlimited hours.

In developing this change, we looked at the needs of students, policies in other countries, as well as research that has shown that academic outcomes suffer the more a student works while studying. It also strikes the appropriate balance so students have the option to work without compromising academic outcomes. More details will be shared in due course.

We also continue to develop the new Recognized Institutions Framework to reward post­secondary institutions that set high standards for selecting, supporting and retaining international students. We will continue to support and protect international students from financial vulnerability and keep protecting the integrity of the International Student Program.

“Working off campus helps international students gain work experience and offset some of their expenses. As international students arrive in Canada, we want them to be prepared for life here and have the support they need to succeed. However, first and foremost, people coming to Canada as students must be here to study, not work. We will continue working to protect the integrity of our student program.” – The Honourable Marc Miller, Minister of Immigration, Refugees and Citizenship

Quick facts

Recent studies conducted in the US and Canada have shown that there is a considerable decline in academic performance for students working more than 28 hours per week, and that working more than 24 hours per week increases the chances that a student will drop out of their program.

Most countries that welcome international students set limits on the number of hours they may work while they study. Australia recently changed its policy to allow a student to work 48 hours every 2 weeks. In the US, students must meet additional criteria before being permitted to work off campus at all.

In December 2023, the Government of Canada raised the cost-of-living threshold that students must meet to be approved for a study permit so they are financially prepared for life in Canada and are not as dependent on working.

International students who begin a college program delivered through a public-private curriculum licensing arrangement on or after May 15, 2024, will not be eligible for a post-graduation work permit when they graduate. Those who already started this type of program prior to May 15, 2024, will still be able to access a post-graduation work permit, provided they meet all other criteria .

The new letter of acceptance (LOA) verification process has been a success. Since its launch on December 1, 2023, through April 1, 2024, IRCC has

•  received almost 162,000 LOAs for verification
• confirmed nearly 142,000 LOAs as valid directly with designated learning institutions (DLIs)
• identified almost 9,000 LOAs that didn’t match any LOA issued by a DLI or that the DLI had already cancelled before the foreign national applied for a study permit

Associated links

• Statement: Minister Miller issues statement on international student allocations for provinces and territories
• Notice: Update on public-private college partnership programs for international students
• Notice: Additional information about International Student Program reforms
• News release: Canada to stabilize growth and decrease number of new study permits issued
• News release: Revised requirements to better protect international students
• News release: Changes to International Student Program aim to protect students
• Website: Work off campus as an international student

Aissa Diop Director of Communications Minister’s Office Immigration, Refugees and Citizenship Canada [email protected]

Media Relations Communications Sector Immigration, Refugees and Citizenship Canada 613-952-1650 [email protected]

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