Policy Analysis with Big Data

This course prepares students to design, conduct, and evaluate data-intensive analyses in public policy, working with both large quantitative and text datasets. The course covers how to build reproducible and scalable data analysis workflows, presents modern applications of causal inference and machine learning methods, and discusses how to interpret the results through real-world examples. It also covers data sources and collection, along with the privacy, fairness, and governance considerations these tools raise when deployed in policy and industry settings. The course assumes prior coursework in statistics and data analysis using Python. It includes hands-on labs and a team-based final project where students answer policy questions of their own choosing using large-scale datasets.