Computational Tools and Techniques for Social Science
Jupyter binder: https://mybinder.org/v2/gh/PS239T/spring_2021/64a6e3c52d40c287cd032432d8c9a5be3df624bf
This course will help social science graduate students to think computationally and develop proficiency with computational tools and techniques, necessary to conduct research in computational social science. Mastering these tools and techniques not only enables students to collect, wrangle, analyze, and interpret data with less pain and more fun, but it also let students to work on research projects that would previously seem impossible.
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The goal of this course is to help students become a more efficient and innovative researcher by leveraging the power of automation.
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The course is currently divided into two main subjects (fundamentals and applications) and six main sessions.
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In the first section, students learn best practices in data and code management using Git and Bash.
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In the second, students learn how to wrangle, model, and visualize data easier and faster.
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In the third, students learn how to use functions to automate repeated things and develop their own data tools (e.g., R packages, Shiny apps).
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In the fourth, students learn how to collect and parse semi-structured data at scale (e.g., scraping websites, PDF files, and social media posts).
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In the fifth, students learn how to analyze high-dimensional data (e.g., text) using machine learning.
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In the final, students learn how to access, query, and manage big data using SQL.
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Meeting dates: 01/19/2021 - 05/07/2021
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Lecture: Monday 9-11 AM (Zoom)
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Section: Friday 8-10 AM (Zoom)
The online book version of the course materials by Jae Yeon Kim is currently work in progress.
Please create issues on the course GitHub repository, if you have questions, comments, or suggestions.
Instructor(s): Jae Yeon Kim (jaeyeonkim@berkeley.edu) and Nick Kuipers (nkuipers@berkeley.edu)
This course is a remix version of the course originally developed by Rochelle Terman then revised by Rachel Bernhard. Other teaching materials draw from the workshops Jae Yeon Kim has created for D-Lab and Data Science Discovery Program at UC Berkeley.
