Princeton University

Quantitative Analysis II

Prof. Marc Ratkovic
Spring 2017  

The second course in the Politics departments graduate quantitative methods sequence, covering causal inference and the potential outcomes framework, linear regression, maximum likelihood, instrumental variables, regression discontinuity, and an introduction to machine learning. (Student evaluations)


Quantitative Analysis III

Prof. Kosuke Imai
Fall 2016  

The third course in the Politics departments graduate quantitative methods sequence, covering discrete choice, the EM algorithm and variational inference, models for longitudinal data, and survival analysis, all with an emphasis on causal inference. Here are some of my slides. (Student evaluations)


Statistical Programming Camp

Winter 2017

This course prepares graduate students for the Politics departments quantitative methods sequence. It presents the basics of statistical programming using R, an open-source computing environment. Using data from published journal articles, students learn how to manipulate data, create graphs and tables, and conduct basic statistical analysis. (Student evaluations)


Visualizing Data

Instructor: Will Lowe
Summer 2016

A first course in R for incoming college freshmen, with a focus on data visualization as well as a gentle introduction to linear regression, experiments, network analysis, and causal inference. (Student evaluations)


The University of Chicago, Harris School

Political Institutions and the Policy Process (PIPP)

Teaching Assistant
Prof. William Howell
Winter 2014

A course designed to introduce students in the Master of Public Policy program to a set of analytical tools and concepts for understanding how political institutions generate public policy, and to apply these tools in examining the major institutions of democracy in the United States. 

Syllabus available here.