Data Science for Politics (Residential)

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 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.