Textbooks & Other Resources

Caution

Please note that the following textbooks are NOT strictly required for this course, but they are strongly recommended for those who prefer to have a physical reference.

• The Data Science Design Manual by Steven S. Skiena

• Python for Data Analysis by Wes McKinney (Free, Open Access)

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Other Resources

• Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman

• Probabilistic Programming & Bayesian Methods for Hackers by Cameron Davidson-Pilon

• Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

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• Jupyter Notebook User Guide

• Python Data Science Handbook by Jake VanderPlas

• Python: How to Think Like a Computer Scientist (Swarthmore Edition) by Jeffrey Elkner, Allen B. Downey, and Chris Meyers (free, open textbook)

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