This course aims to deliver a compact and tailored introduction to the core mathematical concepts of data science: probability theory, calculus, linear algebra, and statistical learning. We will establish and reinforce the foundation that will support further studies in machine learning, deep learning, and causal inference, and which will inform how students conceptualize and analyze data. By the end of the course, students will (re)acquaint themselves with the basic principles of probability theory that structure the process that generates data; with the behavior of functions that describe relationships in data; with the rules of linear algebra that govern how to manipulate data; and, finally, with the tools of statistical learning that we use to extract information from our datasets.

This is the second course in the Hertie School’s MPP sequence in statistical modeling. Assuming prior knowledge in multiple linear regression, it introduces students to a new perspective on studying causes and effects in social science. The course agenda covers various strategies to uncover causal relationships using statistical tools. We start with reflecting about causality and the ideal research design, and then we learn to use a framework to study causal effects. Then, we revisit common regression estimators of causal effects and learn about their limitations. Next, we focus on matching, instrumental variables, difference-in-differences and fixed effects estimators, regression discontinuity designs, and techniques to explore moderated relationships. All classes divide time between theory and application.

This course teaches undergraduates to apply the core theoretical frameworks and empirical methods of academic political science to the analysis of American politics. Topics covered include the formal and informal institutions of American government, including separation of powers, federalism, Congress, the Constitution, the presidency, the courts, the bureaucracy, and the political parties; theories of political behavior, including partisanship, participation, vote choice, and public opinion; and topics in contemporary U.S. politics, including race, identity politics, the carceral state, polarization, and money in politics. Course assignments place a particular emphasis on effective writing and communication in the social sciences.

This course provides an introduction to game theoretic analysis in political science. We study the concepts and models used to analyze political behavior in strategic contexts, including normal and extensive form games, games of incomplete information, repeated games, and bargaining.

This is the first in a two-course graduate sequence on American political institutions, which introduces students to classic theoretical and descriptive works. The readings draw on a variety of theoretical frameworks, especially historical and rational-choice institutionalism, and a mix of quantitative and qualitative methodologies. Topics covered include collective action, political parties, electoral institutions and representation, separation of powers and the three branches of American government, the bureaucracy, policymaking, and federalism.