## 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 oered 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