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.

Course Textbooks#

An additional book you may find useful:

  • A Hands-On Introduction to Data Science by Chirag Shah (Cambridge).

Software Documentation#

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.

Social Aspects#

  • Data Feminism (online version) by Catherine D’Ignazio and Lauren F. Klein. Critical perspectives on data.

  • Olteanu, Alexandra and Castillo, Carlos and Diaz, Fernando and Kiciman, Emre, Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (December 20, 2016). Frontiers in Big Data 2:13. doi: 10.3389/fdata.2019.00013, Available at SSRN:


Writing is a part of this class, but will play a particularly important role in your graduate career. Hopefully these resources are helpful:

Diving Deeper#

These resources will help you explore further some thing we touch on in this class, or to further expand your knowledge: