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Python for Data Analysis (2nd edition) by McKinney. This book includes an introduction to Python.
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Python Data Science Handbook by VanderPlas. Similar to P4DA, but with more concrete examples.
- ProGit by Chacon & Straub. See chapters 1-2.
- KataCoda Interactive Git Tutorial
- GitHub Cheatsheet
- Git Reference Manual
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Think Python by Downey. Free online.
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Python Crash Course by Matthes.
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Invent with Python Books by Sweigart. Free, fun exercises to practice Python.
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Hitchhiker's Guide to Python by Reitz & Schlusser. A reference on Python best practices (not an intro to Python).
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The Elements of Graphing Data by Cleveland. This is a classic about how to design graphics.
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Visualizing Data by Cleveland. Another classic, about how to choose the right graphic for your data. Complements "The Elements of Graphing Data".
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Color Brewer 2, a website to help you choose colors.
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Color Oracle, a free colorblindness simulator for Windows, OS X, and Linux.
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Emery's Essentials, a website to help you choose the right graphic.
- Style: Lessons in Clarity and Grace by Williams & Bizup.
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Computational and Inferential Thinking by Adhikari & DeNero. The textbook for UC Berkeley's lower-div data science class.
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[Data Science: Principles and Python][sharpnack] by Sharpnack. A work-in-progress textbook for STA 141B by Professor Sharpnack.
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Statistics Done Wrong by Reinhart.
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The Elements of Statistical Learning by Hastie, Tibshirani, & Friedman. The canonical reference on machine learning.
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Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani. Undergraduate version of The Elements of Statistical Learning.
- W3Schools SQL Tutorial
- Getting Started with SQL by Nield.
- Practical SQL by DeBarros.
- Interactive Data Visualization for the Web (2nd edition) by Murray.
- MDN JavaScript Tutorials