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I served as consultant to undergraduate students in the NYU Politics Department for class projects involving data analysis and prepared teaching material for classes in R and Stata.

The course covers political institutions and behavior in the United States, and introduces students to analytical concepts and approaches useful for the study of American Politics. I was teaching assistant in two sections.

The course introduces students to quantitative techniques for the scientific study of politics, from basic data visualization to regression analysis and causal interpretation, using R language. I was teaching assistant in two sections.

The course introduces students to the study of Comparative Politics, with a focus on the process of democratic transition, its causes and threats to democratic consolidation. I was teaching assistant in two sections and held a lecture on quantitative research methodology in Political Science.

Introductory class in statistics and quantitative research methodology for the Master of Arts in Politics. Covers basic descriptive statistics, regression, confidence intervals, testing, and the logic of causality, using the software Stata.

Central course in the Politics PhD quantitative methods sequence. Covers causal inference in social science research, focusing on non-parametric and semi-parametric methods and frequentist inference. I was course assistant and prepared original lab material with examples, simulation, and replication using R and Stata.

As part of the PhD program requirements, I have written an original syllabus for a graduate-level course in political science. The syllabus is designed to cover topics of identity (ethnic, religious, and national) in comparative politics.