Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and practical applications. The topics include we will cover are an in-depth analysis of linear models and different variants, their extension to generalized linear models, and an introduction to nonparametric regression.

This course introduces first-year graduate students in the humanities and social sciences to the knowledge and skills fundamental to success in graduate school. Particular topics vary.

Introduces fundamental concepts of computation, data structures, algorithms, & databases, focusing on their role in data science. Covers both theoretical studies & hands-on learning activities. Includes basic data structures, advanced data structures, searching, sorting, greedy algorithms, linear programming, & basics of databases. Will develop computational thinking skills and learn a variety of ways to represent & analyze real-world data.

Covers the fundamentals of probability and stochastic processes. Students will become conversant in the tools of probability, clearly describing and implementing concepts related to random variables, properties of probability, distributions, expectations, moments, transformations, model fit, sampling distributions, discrete and continuous time Markov chains, and Brownian motion.

In this course students will learn how to create change in the public policy arena by understanding political actors, their interests, and the institutions they inhabit. Students will learn how issues move through the policy process, at which points they are most amenable to influence, and how to create and use professional work products to influence them.

Introduction to regression modeling. Topics will be discussed first in the context of linear regression, and then revisited in the context of logistic regression, ordinal regression, proportional hazards regression, and random forests. Students will be required to fit the models (both MLE and Bayesian) and use the strategies discussed in class.

Covers data pipeline: techniques to collect data, organize, query & apply the data, and generate products that describe the insights. Topics include Python environments, containers using Docker, data wrangling with pandas, data acquisition via flat files, APIs, JSON formats, and webscraping, relational, document, and graph databases, exploratory data analysis including static & interactive data visualization, dashboards, and cloud computing.

This course will provide a solid foundation of insights into how Congress works, essential for aspiring public policy advocates. Topics investigated include historical precedents for policymaking, the process of Congressional decision-making, and power dynamics in Congress. We will also identify and develop the leadership skills and tactics of successful advocates, placing recent controversies and public policy issues in an historical context.

What are the most pressing policy problems facing Virginia and how can they be addressed? Students will learn how the broad historical forces of Virginia's past, her current political institutions, and changing social divisions shape public policy in Virginia today. Student projects will focus on current and future challenges facing the Commonwealth and develop strategies to address them.

Consider the effect of public opinion on policy in the U.S. What do policymakers believe about citizens' preferences? Whose opinion matters, when does it matter, & why? Do policies always reflect the majority? How has partisan polarization affected policy-making in recent years? Examine questions through lens of some of today's most pressing issues, including immigration policy, social welfare programs, military spending, abortion, & more.