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¶
Python for Data Analysis, 2nd Edition by Wes McKinney (O’Reilly, ISBN 978-1491957660).
You can read this book for free through the Boise State Library.
Think Like a Data Scientist by Brian Godsey (Manning, ISBN 978-1633430273).
You can read this book for free through the Boise State Library.
Software Documentation¶
Quick links to software documentation:
Statistics¶
More reading on probability and statistics:
Visualization¶
Seaborn tutorial — organized topically, very good resource
My plot utilities (for preparing papers with
plotnine)W.E.B. Du Bois’s Data Portraits: Visualizing Black America, edited by Whitney Battle-Baptiste. Historical data visualizations.
Python¶
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¶
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.
Diving Deeper¶
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.
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: http://dx.doi.org/10.2139/ssrn.2886526