Documentation & Reading¶
This page collects various documentation and readings. Many of these, or portions of them, are linked to from content in individual weeks, but they are re-linked here for your convenient reference. This is a mix of material from others (most of it) and that I have written.
Quick links to software documentation:
More reading on probability and statistics:
Further resources for the Python programming language:
Learn Python the Hard Way by Zed Shaw. More thorough treatment of Python.
Fluent Python. Learn advanced and idiomatic Python.
Writing is a part of this class, but will play a particularly important role in your graduate career. Hopefully these resources are helpful:
Style: Lessons in Clarity and Grace — one of the best books I know to help you improve your writing.
These resources will help you explore further some thing we touch on in this class, or to further expand your knowledge:
W.E.B. Du Bois’s Data Portraits: Visualizing Black America, edited by Whitney Battle-Baptiste. Historical data visualizations.
Statistics Done Wrong: The Woefully Complete Guide, by Alex Reinhart. Also available in the O’Reilly Learning Center.
How to Lie with Statistics by Darrell Huff.
Counterfactuals and Causal Inference, 2nd Edition, by Stephen L. Morgan and Christopher Winship.
An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.