PSCI 207: Applied Data Science

With John Lapinski, Ashley Tallevi, and Samantha Sangenito (Spring 2018, 2019; Fall 2019)
Course syllabus

An undergraduate course that introduces students to the fundamentals of data science. The course focuses on applications in survey research, election studies, and political science. A major component of the class is learning statistical programming in R. Students lead the basics of data cleaning and management, data visualization, and web scraping.

Gov 2001: Advanced Quantitative Research Methodology

With Gary King and Soledad Prillaman (Spring 2014 and 2015)
Course website and syllabus
See videos from my lectures here

Graduate course with the purpose of outfitting students with the tools required to do quantitative research in the social sciences, as well as to develop their own new statistical methods. Topics include statistical modeling using maximum likelihood estimation, matching, and missing data imputation.

Gov2002: Causal Inference

With Matt Blackwell (Fall 2015)
Course syllabus
See interactive web apps used for teaching and other course materials here

Graduate course on the theory and implementation of causal inference methods for social science research. Topics include randomized experiments, matching, diff-in-diff, instrumental variables, regression discontinuity designs, sensitivity analysis, and causal mediation.

17.20: Introduction to the American Political Process

Taught at MIT with Devin Caughey (Spring 2016)
Course syllabus
See my course materials here

Undergraduate course on political institutions and behavior in the United States.

Gov1540: The American Presidency

Fall 2013, 2014, and 2015
With Roger Porter (Fall 2013, 2014, and 2015)
Course syllabus

Undergraduate course on the development and modern practice of presidential leadership in the United States.

Math (P)refresher: A Short Course on the Quantitative in Social Science

With Soledad Prillaman (Summer 2013) and Anton Strezhnev (Summer 2014)
Course website and Course syllabus
See my R tutorial for beginning programmers here

Two week course for incoming Harvard PhD students meant to review fundamental mathematical principles prior to enrolling in quantitative analysis courses. Topics discussed include derivative and integral calculus, probability, linear algebra, and optimization. Prefresher students are also introduced to statistical computing in R and typesetting in LaTeX.