Main content start

Computational Public Policy

The concentration in Computational Public Policy focuses on students developing skills in computer science, data science, and advanced statistics combined with policy analysis. It focuses primarily, although not exclusively, on the application of quantitative techniques to policy issues that arise in various subject matter areas. Because policy analysis interacts with the technical elements of particular disciplines, all schools and many departments in the University offer courses that meet these requirements. For the most part, the common denominator is the extensive use of machine learning and artificial intelligence, and statistics, as policy analysis tools. Concentration courses should focus on developing the skills necessary for modern data-intensive policy analysis. Students choosing such courses, therefore, need to consider their interest in the field in which the course is being given in addition to the methodology being presented. It is also advisable in the majority of the recommended courses that concentrators have an above average background in mathematics, statistics and computation.

Affiliated Faculty

Graduate School of Business - Faculty
Law School, Political Science
Economics, Program on Energy and Sustainable Developmen


Below is a list of suggested courses. The list includes classes that students have taken in the past to complete this particular concentration, as well as additional classes that may be considered toward this topic. Students may choose to take other courses, though all concentration courses must be discussed with and approved by the faculty adviser whether or not they are on the suggested list. Students may also propose directed reading, Overseas Studies (BOSP), and Bing Stanford in Washington (BSIW) courses.