Data Science for Politics

This course explores techniques to harness the power of ``big data'' to answer questions related to political science and/or American politics. Students will learn how to use R---a popular open-source programming language---to obtain, clean, analyze, and visualize data. No previous knowledge of R is required.

We will focus on applied problems using real data wherever possible, using R's ``Tidyverse.'' In total, in this course we will cover concepts such as reading data in various formats (including ``cracking'' atypical government data sources and pdf documents); web scraping; data joins; data manipulation and cleaning (including string variables and regular expressions); data mining; making effective data visualizations; using data to make informed prediction, and basic text analysis. We will also cover programming basics including writing functions and loops in R. Finally, we will discuss how to use R Markdown to communicate our results effectively to outside audiences. Class sessions are applied in nature, and our exercises are designed around practical problems: Predicting election outcomes, determining the author of anonymous texts, and cleaning up messy government data so we can use it.