## Introduction to Research Design [syllabus]

An undergraduate course. Introduces methods of scientific inquiry targeted to quantative social science majors. Introduces concepts of correlation and regression leading to causal inference through the potential outcomes framework and the predictive foundations of machine learning. Topics covered include: Experiments, Regression and Matching, Difference in Differences, Regression Discontinuity, Predictive Modeling and Machine Learning, Bayesian Inference.

## Computational Methods for the Social Science [lectures] [syllabus]

A second course in computational methods for exploration and analysis of social science data. Introduction of a number of advanced computational techniques in R including parallelization, SQL-like syntax for large data sets, statistical sampling via Monte Carlo, and a light introduction to text analysis.

## Math Camp [notes1] [notes2] [syllabus]

An introductory course for incoming political science PhD students. Introduces basic notions of probability and statistics such as probability measures, applied probability, conditional probability, expectations, density functions, and concepts of linear algebra.

## A Quick Introduction to Bayesian Statistics [lecture]

A two-week lecture series introducing some basic concepts of Bayesian statistics given as a part of University of Michigan's statistical learning workshop. A brief overview of key concepts in Bayesian statistics and an introduction to conjugacy and kernel matching methods.