## Princeton University

### Quantitative Analysis II

Preceptor

Prof. Marc Ratkovic

Spring 2017

The second course in the Politics department’s 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

Preceptor

Prof. Kosuke Imai

Fall 2016

The third course in the Politics department’s 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

Instructor

Winter 2017

This course prepares graduate students for the Politics department’s 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

Preceptor

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.