Explore the field of engineering through on-demand learning. Taught by Penn Engineering faculty and offered in partnership with Coursera and edX, our noncredit courses and credentials are flexible, self-paced, open to everyone – and affordable.
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Learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language.
Learn to integrate the scientific and technological principles that are necessary to assess and implement small-scale renewable energy schemes.
Learn to develop customized urban transit plans and improve city mobility by bridging technical knowledge with practical application.
Earn a specialization certificate 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 certificate 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.
Earn a specialization certificate by completing the three courses listed here and paying the certificate fee.
Discover the fundamentals of data analytics in this introductory course that covers data wrangling with SQL and exploratory data analysis (EDA) using Python. Learn to define problems, query databases, and visualize data to uncover meaningful insights—all while mastering essential tools like SQL, pandas, and matplotlib.
Develop your predictive analytics expertise by building and evaluating models like linear and logistic regression, decision trees, and random forests. Gain practical experience with supervised and unsupervised learning techniques, empowering you to solve real-world problems with Python.
Build a strong foundation in data visualization and storytelling while gaining practical experience with Tableau. Learn to create clear and effective visualizations, apply best practices in data presentation, and craft compelling narratives to communicate insights and support data-driven decision-making.