Data Science for Politics
5:40pm–6:55pm
BARNARD HALL BAR 302
Barnard
Seat data provenance: Directory as of September 29, 2026 at 4:00 AM
Description
This course explores techniques to harness the power of “big data” to answer questions related to political science and/or American politics. We will teach students how to use R—a popular open-source programming language—to obtain, clean, analyze, and visualize data. We will focus on applied problems using real data wherever possible, with a particular focus on 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. No previous knowledge of R is required
Reviews
Course experience
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Instructor quality
n=28, 2018–2025
Aggregated across every course Michael Miller has taught.
Teaching quality
4.1 / 5
Grading fairness
No signal
Workload
No signal
Difficulty
3.1 / 5 · Moderate
Overall sentiment
Mixed
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Where it meets
Flat map — motion reduced
Workload and grading
- Grading
- Not published
- Format
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- Credits
- 3 credits
Reported workload
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