Categorical

Bayesian modeling and inference is a powerful modern approach to representing the statistics of the world, reasoning about the world in the face of uncertainty, and learning about it from data. It cleanly separates the notions of representation, reasoning, and learning. It provides a principled framework for combining multiple source of information such as prior knowledge about the world with evidence about a particular case in observed data. This course will provide a solid introduction to the methodology and associated techniques, and show how they are applied in diverse domains ranging from computer vision to molecular biology to astronomy. Graduate-level requirements include different exams requiring greater depth of understanding of topics, and will be assigned questions based on graduate-student specific assignments topics.

Quantitative methods in political science and policy research are changing rapidly. The rise of the internet has brought in new sources of text, network, geographical, image, video, and other data. Meanwhile, computing storage and processing capabilities continue to expand, while data and code sharing norms have made it so that anyone with a computer and internet connection can have access to a growing set of tools and methods for modeling and interpreting patterns. This course focuses on the extraordinary work that is emerging in politics and policy as a result of these recent advances, with a broad set of applications ranging from health and defense to environmental and agricultural policy. The course highlights current trends, challenges, and new directions for political and policy researchers in academia, government, and the private sector, focusing on how these new data sources and methodologies are being used to solve problems in social science and public policy.

This course will guide students through advanced applications of computational methods for social science research. Students will be encouraged to consider social problems from across sectors, like health science, education, environmental policy and business. Particular attention will be given to the collection and use of data to study social networks, online communities, electronic commerce and digital marketing. Students will consider the many research designs used in contemporary social research and will learn to think critically about claims of causality, mechanisms, and generalization in big data studies. Graduate requirements include additional readings and a more in-depth final paper than is required at the undergraduate level.