MIT Graduate Courses


Quantitative Research Methods IV (17.806)

This course is the fourth and final course in the quantitative methods sequence at the MIT political science department. The course covers various advanced topics in applied statistics, including those that have only recently been developed in the methodological literature and are yet to be widely applied in political science. The topics for this year are organized into three broad areas: (1) research computing, where we introduce various techniques for automated data collection, visualization, and analysis of massive datasets; (2) statistical learning, where we provide an overview of machine learning algorithms for predictive and descriptive inference; and (3) finite mixture models (e.g., Latent Dirichlet allocation for text analysis), as well as a variety of estimation techniques such as EM Algorithm and Variational Inference. [syllabus]


Quantitative Research Methods III (17.804)

This course covers advanced statistical tools for empirical analysis in modern political science. Our focus in this course will be on techniques for model-based inference, including various regression models for cross-sectional data (e.g., binary outcome models, discrete choice models, event count models, etc.), as well as grouped data (e.g., mixed effects models and hierarchical models). This complements the methods for design-based inference. This course also covers basics of the fundamental statistical principles underlying these models (e.g., maximum likelihood theory, theory of generalized linear models, Bayesian statistics) as well as a variety of estimation techniques (e.g., numerical optimization, bootstrap, Markov chain Monte Carlo). The ultimate goal of this course is to provide students with adequate methodological skills for conducting cutting-edge empirical research in their own fields of substantive interest. [syllabus]


Quantitative Research Methods I (17.800)

This is the first course in a four-course sequence on quantitative political methodology. Political methodology is a growing subfield of political science which deals with the development and application of statistical methods to problems in political science and public policy. This first course provides a graduate-level introduction to regression models, along with the basic principles of probability and statistics which are essential for understanding how regression works. Regression models are routinely used in political science, policy research, and other disciplines in social science. The principles learned in this course also provide a foundation for the general understanding of quantitative political methodology. If you ever want to collect quantitative data, analyze data, critically read an article which presents a data analysis, or think about the relationship between theory and the real world, then this course will be helpful for you. [syllabus]


Game Theory and Political Theory (17.810)

This course provides a graduate-level introduction to formal theoretical analysis in political science. This course is designed as a rigorous introduction to the concepts and models used to analyze political behavior in strategic contexts. The course focuses on non-cooperative game theory covering normal and extensive form games, games of incomplete information, repeated games, and bargaining. Qualified undergraduates can also take the course. [syllabus]


MIT Undergraduate Courses


Machine Learning and Data Science in Politics (17.835)

Empirical studies in political science is entering a new era of "Big Data" where a diverse range of data sources have become available to researchers. Examples include network data from political campaigns, data from social media generated by individuals, campaign contribution and lobbying expenditure made by firms and individuals, and massive amount of international trade flows data. How can we take advantage of these new data sources and improve our understanding of politics? This course introduces various machine learning methods and their applications in political science research. [syllabus]


Princeton University Graduate Courses


Quantitative Analysis II (POL 572)

(Professor Kosuke Imai) This course is the first course in applied statistical methods for social scientists. We begin by studying the fundamental principles of statistical inference. Students will then learn a variety of basic cross-section regression models including linear regression model, structural equation and instrumental variables models, discrete choice models, and models for missing data and sample selection. Unlike traditional courses on applied regression modeling, I will emphasize the connections between these methods and causal inference, which is the primary goal of social science research. [syllabus]


Mathematics for Political Science (POL 502)

(Professor Kristopher Ramsay) This course presents basic mathematical concepts and techniques that are essential for formal and quantitative research in political science. It prepares students for advanced courses o ered in the department (e.g., POL 571, 573, 575, 577) and possibly courses in Economics. The topics will include calculus, linear algebra, real analysis, and optimization. There is no prerequisite for the class, so the objective is to gain experience doing more advanced and creative mathematics. [syllabus]


Math Camp

The math camp will prepare students to take the POL502 (Mathematics for Political Science) class and other graduate level classes in F&Q field. The goal of the class will be to remind students of basic and intermediate mathematical concepts and increase both mathematical fluency and problem solving ability. We will also try to give some intuitions for how you will see some of these mathematical tools in your course work in political science. There are no prerequisites and students with more recent mathematical exposure should gain as much as those whose mathematics education ended longer ago.




Research Computing


[Instructions for Building R Packages]