The purpose of this course is to develop the student's ability to define and solve public problems. Subsidiary objectives of the course are to help the student to integrate the analytical, political, and leadership skills they have learned in their other MPP courses and improve their ability to work in teams; and hone their written and oral presentation skills. Prerequisites: Graduate student in public policy

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.

Students register for this course to complement an industry work experience. Topics focus on the application of engineering principles, analysis, methods and best practices in an industrial setting. A final report is required.

Specialized or advanced topics not in DS current course offerings. Requires (a) approval of the program director and (b) an SDS faculty member who will serve as instructor.  Propose a syllabus which includes a week-by-week accounting of the topics, materials (papers and textbooks), and assessments.  Reach out to the program director for more details.

A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. Papers will be drawn from recent and seminal publications in computational biology.