Penn Engineering’s on-demand non-credit courses are designed for professionals, career changers, aspiring technologists, and curious learners looking to build practical skills in programming, artificial intelligence, and machine learning.
Our initial course offerings in programming, AI and maching learning are only the start. Penn Engineering will continue developing new courses, specializations, and learning pathways — creating a flexible online hub learners can return to throughout their careers to expand their expertise, explore emerging technologies, and stay competitive in a rapidly evolving workforce.
Unlike many online learning platforms that offer broad introductions, these courses provide academically rigorous, specialized, and applicable learning developed by Penn Engineering faculty and delivered through Canvas.
Advance your skills and knowledge in Penn Engineering’s areas of expertise through courses taught by Ivy League faculty.
Browse our available on-demand courses and specializations in Canvas. Start learning whenever you are ready — no application required.
Start immediately with NO application process. Enroll and begin learning whenever you are ready.
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Learn from Penn Engineering’s world-class faculty. Build practical skills in programming, artificial intelligence and machine learning.
Open-access and affordable, these non-credit offerings make Penn Engineering learning more accessible than ever. Courses are available individually or as part of bundled specializations:
Penn Engineering Alumni are eligible to register for discounted access to on-demand courses by filling out a short application.
Please note that you must use your alumni email address (how to activate your alumni email address).
Earn a specialization by completing the four courses listed here and paying the certificate fee.
Explore core programming concepts like data structures, conditionals, loops, variables, and functions. This course will get you ready to code at a fast pace.
Discover core concepts like Data Frames and joining data, and learn how to use data analysis libraries like pandas, numpy, and matplotlib. You’ll also get an overview of loading, inspecting, and querying real-world data.
Learn how to write custom Java classes and methods, and how to test code using unit testing and test-driven development. Topics include basic data structures like Arrays and ArrayLists and overloading methods.
Get a comprehensive look at Java inheritance, including access modifiers and overriding methods. Explore abstract classes and learn how to read and write to files, use regular expressions for parsing text, and how to leverage complex data structures like collections and maps.
Earn a specialization by completing the four courses listed here and paying the certificate fee.
Take a look at artificial intelligence through philosophical and science fiction lenses, and review Python basics. Then explore AI algorithms through studying rational agents and common search algorithms like A* search. Complete short coding assignments in Python.
Review the basics of discrete math and probability before enhancing your probability skills and learning how to interpret data with tools such as the central limit theorem, confidence intervals and more. Complete short weekly mathematical assignments.
Use statistical learning techniques like linear regression and classification to solve common machine learning problems. Complete short coding assignments in Python.
Delve into the history of deep learning, and explore neural networks like the perceptron, how they function, and what architectures underpin them. Complete short coding assignments in Python.
