This is the git repository for Computational Thinking for Social Scientists. This book intends to help social scientists to think computationally and develop proficiency with computational tools and techniques necessary to research computational social science. Mastering these tools and techniques not only enables social scientists to collect, wrangle, analyze, and interpret data with less pain and more fun, but it also let them work on research projects that would previously seem impossible.
The book is divided into two main subjects (fundamentals and applications) and six main sessions.
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How to collect and parse semi-structured data at scale (e.g., APIs and webscraping)
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How to analyze high-dimensional data (e.g., text) using machine learning
Please feel free to create issues if you find typos, errors, missing citations, etc. via the GitHub repository associated with this book.
Content developer: Jae Yeon Kim: jkim638@jhu.edu
This book is as much collected as it is authored. It is a remix of PS239T, a graduate-level course in computational methods at UC Berkeley originally developed by Rochelle Terman (now Assistant Professor of Political Science at the University of Chicago), and later revised by Rachel Bernhard (now Associate Professor at Nuffield College and the University of Oxford).
I have taught PS239T as a lead instructor in Spring 2019, a TA in Spring 2018, and a co-instructor with Nick Kuipers (now Assistant Professor of Politics at Princeton) in Spring 2020.
Other materials are adapted from workshops I developed for D-Lab, the Data Science Discovery Program at UC Berkeley, and the Summer Institute in Computational Social Science at Howard University and Mathematica.
I have cited all sources—books, articles, slides, blog posts, and videos—whenever I am aware of them.
