▤Study Index ← Overview

Programming · Index

The Programming column of the University Plan · click any entry to open

Free legal textbooksfree from the authors

Released free by their authors (mostly CC-licensed); already downloaded locally.

Think Python 2e (Allen Downey)

Widely considered the best Python intro · free · 900 KB

Think Stats 2e (Allen Downey)

Learn statistics with Python · feeds straight into cognitive science data analysis · 2.0 MB

Think Complexity 2e (Allen Downey)

Complex systems · networks · cellular automata · exploring complexity with code · 6.0 MB

Official free resourcesneeds internet

All links tested working (2026-09-10).

Python official tutorial

The authoritative reference · Chinese translation available

Harvard CS50P · Python

An intro course with problem sets and feedback · widely praised

Automate the Boring Stuff with Python

Learn Python by building small tools · full text free online

Python Data Science Handbook

The best textbook for the scientific stack · numpy / pandas / matplotlib, free online

Think Bayes 2e (online)

Bayesian statistics · the most relevant book for cognitive science · free online full text (the PDF is not downloadable)

MIT · The Missing Semester

Git / shell / debugging — what school never teaches but you use every day · strongly recommended

The Missing Semester · Chinese edition

Same course, translated

Pro Git · official Chinese edition

The authoritative Git textbook · free online

PsychoPy documentation

Experiment programming for cognitive science · used from Year 2 fall

jsPsych documentation

Online psychology experiments · runs in the browser

PyTorch tutorials

Deep learning · cognitive modelling · used from Year 2 spring

Dive into Deep Learning (D2L)

Free and open source · Chinese edition · Mu Li's team (in the 数学工具/教材 folder)

Cheatsheetlook up as needed

Use this when you know what you want to do but not how to write it. The key is the need, not the function name.

Code Cheatsheet Chinese

53 snippets grouped into 10 "what do I want to do" buckets · the keyword column is searchable with Cmd+F · each line notes its source and its trap

Correctness self-checkre-runnable

Every snippet in the notes was actually run. You can re-run them yourself at any time.

自检脚本.py Chinese output

Python traps · numpy broadcasting · axis semantics · vectorisation speedups · pandas pipelines · random seeds

How to use: read the original notes to build the frame → then Think Python for fundamentals → go deep on the scientific stack with the Python Data Science Handbook → push code to GitHub all the way through.

Environment: the Python environment lives in 数学工具/ (miniconda) and is shared between programming and maths. Double-click 「启动 Jupyter.command」 to start it. Installed: numpy / scipy / matplotlib / pandas / jupyter / pytest.

Not installed yet (install on demand): PsychoPy (~100 MB), PyTorch (~2–3 GB) — per the plan they're needed in Year 2 fall and spring respectively; install them when you need them, commands are in the notes.

Honest limitation: the code in the notes was actually run and verified (including the examples that are supposed to error); but the PsychoPy / jsPsych / PyTorch examples are structural sketches (no environment installed, so they could not be run) — for systematic learning, go by the official docs.