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: [email protected]
This book is collected as much as it is authored. It is a remix version of PS239T, a graduate-level computational methods course at UC Berkeley, originally developed by Rochelle Terman (Assistant Professor of Political Science, Chicago) then revised by Rachel Bernhard (Associate Professor of Quantitative Political Science Research Methods at Nuffield College and the University of Oxford). I have taught PS239T as lead instructor in Spring 2019 and TA in Spring 2018 and taught it with Nick Kuipers (Assistant Professor of Political Science and Presidential Young Professor at the National University of Singapore) in Spring 2020. Other teaching materials draw from the workshops I have created for D-Lab and Data Science Discovery Program at UC Berkeley and the Summer Institute in Computational Social Science hosted by Howard University and Mathematica. I have also cited all other references, such as related books, articles, slides, blog posts, and YouTube video clips, whenever I am aware of them.
