Extra materials to reinforce and extend what you learn in class
Dr. Cheng Li | Oregon State University | ENGR 103
Intro ModuleThe required textbook (Guttag, Introduction to Computation and Programming Using Python) is your primary reference. These supplemental resources are here for when you want a different explanation, more practice problems, or a visual/video alternative.
Written by the creators of Python. Surprisingly readable for beginners — covers variables, control flow, functions, and data structures in a logical order. Great to read alongside the textbook.
Run Python directly in your browser — no setup needed. Short, focused exercises on each topic. Good for a quick warmup or checking your understanding of a specific concept.
36 beginner exercises, each with a solution discussion. Excellent for building problem-solving muscle. Work through one or two per week as supplemental practice — they're well-matched to the pace of this course.
Small, auto-graded problems you can solve in the browser. Especially useful for practicing functions and list manipulation (Weeks 3–5 material). Instant feedback, no setup.
The MIT course that our textbook was written for. Lectures by John Guttag himself. If you want to see the material presented by its author, this is it. Dense but rewarding — watch at 1.25× speed.
A single long-form video that walks through Python from scratch. No fluff — just code. Good if you prefer watching someone type and explain. Covers everything from variables to OOP.
One of the most popular Python tutorial channels. Individual videos per topic (lists, dictionaries, functions, classes) — easy to look up exactly what you're working on. High production quality, very clear explanations.
The authoritative source. Bookmark it. When you're not sure what a function does or what arguments it takes, this is where to look — not Stack Overflow first, docs first.
A well-organized, searchable cheat sheet covering syntax, built-ins, data structures, and common patterns. Useful to have open in a second tab while coding. Much friendlier than the official docs for quick lookups.
When you hit an error you don't understand, copy the error message and search Stack Overflow. There is an overwhelming probability someone has had your exact problem before. Read the accepted answer, then read the next one too — context matters.
A free online textbook covering NumPy, SciPy, and Matplotlib — the scientific computing stack you'll encounter in Weeks 8–9. Written by researchers, for researchers. Keep it bookmarked for when the course reaches data analysis.
NumPy is Python's core array library — essential for any engineering computation. The official site has beginner tutorials, quickstart guides, and detailed docs. Start with the quickstart when we hit Week 8.
The standard Python plotting library. These official tutorials walk you through making line plots, scatter plots, histograms, and more. Bookmark the "Pyplot tutorial" for Week 9.
Not for ENGR 103 directly — but if you finish this course and want to see where Python can take you, fast.ai is one of the best free resources for applied machine learning. No math prerequisites. Start here after you're comfortable with the basics.
python3 to get an interactive prompt. Test small ideas here before putting them in a script. It's the fastest feedback loop you have.