Course Description
In this course, students will learn data science using the statistical programming language R, with a focus on political science applications. By the end of the course, students will be able to work with large datasets, build beautiful visualizations, make predictions, and conduct statistical analyses.
Athena Title
Political Analysis in R
Prerequisite
POLS 1101 or POLS 1105H or POLS 1101E
Grading System
A - F (Traditional)
Course Objectives
By the conclusion of this course, students will be able to: 1) Program at a basic level in R, implementing best practices in their workflow, including comments, functions, and reproducible code 2) Import and tidy datasets from external sources 3) Thoughtfully visualize data using ggplot2 4) Train models to predict values in unobserved data 5) Compute confidence intervals and other measures of uncertainty in estimation
Topical Outline
Part 1: Exploration Week 1: Programming Basics Week 2-3: Visualizing Data Week 4: Data Wrangling (Importing, Tidying, and Statistical Transformations) Part 2: Prediction Week 5: Linear Models Week 6-7: Machine Learning Week 8: Prediction Competition Part 3: Uncertainty Week 9-10: Probability Week 11: Estimation Week 12: Confidence Intervals Part 4: Causality Week 13: Experiments Week 14: Difference-In-Difference Week 15: Discontinuity Designs
Syllabus