The Master of Science in Data Science Public Policy (MS-DSPP) is a 39-credit degree program, divided into core (required) courses, elective courses, and seminars.

Core Courses (33 Credits)

The core courses emphasizes analytical skills and core knowledge for designing and managing sound public policy.

Quantitative Social Sciences (6 credits)

  • PPOL 560: Accelerated Statistics for Public Policy I (3 credits)
  • PPOL 561: Accelerated Statistics for Public Policy II (3 credits)

Foundations of Public Policy (9 credits)

  • PPOL 506: Intermediate Microeconomics I (3 credits)
  • PPOL 510: Policy Process; or PPOL 511: Comparative Policy Process (3 credits)
  • PPOL 514: Public Management; or PPOL 515: Comparative Public Management (3 credits)

Civic Data Science (15 credits)

  • PPOL 564: Foundations of Data Science (3 credits)
  • PPOL 646: Data Visualization (3 credits)
  • ANLY 501: Intro to Data Analytics (3 credits)

  • ANLY 502: Massive Data Fundamentals (3 credits)
  • ANLY 512: Statistical Learning (3 credits) 

Ethics and Law (1.5 credits)

  • PPOL 566: Data Ethics (1.5 credits)

Communication (1.5 credits)

  • PPOL 567: Effective Presentation for Science and Technology (1.5 credits) 
     

Quantitative Social Sciences

PPOL 560: Accelerated Statistics for Public Policy I

This is the first course in the two-course sequence on quantitative methods for social science. The sequence is designed to increase understanding of empirical analyses — both as a consumer of empirical analyses and as a producer of such analyses. This course introduces students to descriptive and inferential statistics often used in public policy research. The progression of courses aims to provide students with a solid foundation for analyzing data, conveying analyses in convincing and appropriate ways.

The course will cover two main areas of statistics. First, it will cover the fundamentals of statistical theory such as measures of central tendency and dispersion, probability distributions, random variables, and correlation. Second, it will cover multivariate ordinary least squares (OLS) regression, focusing on understanding when causal claims can and cannot be made in such analysis. In doing so, the course will cover topics such as omitted variable bias, measurement error and the use of interactions to identify heterogeneous treatment effects. This course is taught in R. As part of our effort to teach effective communication skills, students will make presentations about applications using techniques being studied.

PPOL 561: Accelerated Statistics for Public Policy I II

This is the second course in the two-course sequence on quantitative methods for social science. The emphasis is on applied learning; formal proofs and mathematical rigor are presented but not the principal focus of the course. Instruction will concentrate on how to determine the appropriate econometric approach in addressing various types of policy questions.

This course builds on students’ understanding of multivariate regression and introduces advanced, but commonly used, methods of statistical analysis. Topics covered include: random assignment experiments, non-experimental methods such as regression discontinuity designs, instrumental variables, difference-in-difference models, and propensity score matching. Also covered are maximum likelihood estimation, probit and logit, multinomial and ordered logit and probit, truncated/censored dependent variables (tobit models), panel data methods, and time-series analysis. This course will be taught using R. As part of our effort to teach effective communication skills, students will make presentations about applications using techniques being studied. PREREQUISITE: PPOL 9999, Accelerated Statistics for Public Policy I.

Foundations of Public Policy

PPOL 506: Intermediate Microeconomics

This course provides an in-depth analysis of supply and demand, the theory of the consumer and theory of the firm. The course focuses on the determinants of consumer behavior by studying the role of utility maximization and constrained optimization. Firm behavior is studied by investigating the role of profit maximization when firms operate in perfectly competitive markets and when they are monopolies. Key concepts include efficiency, opportunity cost, the role of incentives and marginal analysis. Applications to public policy issues are emphasized.

PPOL 510: Public Policy Process

This course analyzes the politics, institutions, norms, and actors involved in the agenda-setting, legitimation, and decision-making of public policy in the US. Students learn how to use analytical frameworks that explain how the policymaking process works, relates to the substance of policy, and applies to real world issues. Students also investigate how the policy making process varies across different issue areas. As one of several assignments, students write a policy memo suitable for submission to a member of Congress or other key political actor.

PPOL 514: Public Management

This course introduces students to public management: the art and science of planning and implementing public programs. Participants will examine the constraints public managers face in a democratic society; how the challenges of public management vary across different organizational and policy settings; and how public management and policy analysis frequently intersect. A key objective is to offer students a useful mix of theoretical knowledge and practical skills. Throughout the course, emphasis is placed on management as a function performed in all sectors (public, nonprofit, and private) but, when appropriate, participants focus on what factors make public management distinctive and more challenging. Some of the specific topics covered include: executive leadership; the role of front-line operators and middle managers; organizational culture; problems of bureaucratic coordination and dysfunction; how Congress, the President, and the courts attempt to control the public bureaucracy; managing people; managing budgets; privatization and contracting out; ethics in public management; performance management and strategic planning; and management reform strategies. Case studies are used extensively to explore specific challenges and scenarios.

Civic Data Science

PPOL 564: Foundations of Data Science

This critical first semester course aims to prepare all incoming students for the rigor of their upcoming coursework in the rest of the program. The course will accelerate the students to a level of mathematical and computer science competency that will enable them to excel in their ensuing data science courses.

On the mathematical side, students will cover linear algebra with a special focus on matrix algebra (e.g. matrix notation, projections, determinants, inversions, and eigenvectors) and multivariate calculus up until constrained optimization. On the computer science side, students will be introduced to working at the command line and python programming, with introductions to data structures, data manipulation and programming paradigms. Students will work in notebooks and use Git/GitHub to submit coding assignments, developing literate programming and reproducible research skills they will use throughout the program.

PPOL 646: Data Visualization

This course introduces students to the tools, methods and skills necessary for making compelling quantitative graphics for political analysis and public policy research. Students will be trained in programming and software applications useful for big data visualization. Students will build a toolkit of skills in the languages of R, Python and Tableau to prepare for the practical demands of modern data analysis and visualization. Learning goals: 1. Students will learn the theoretical, practical, and aesthetic elements of data visualization. 2. Students will develop skills in multiple software tools useful for making visualizations. 3. Students will gain a foundation in programming for exploratory data analysis. Students will work during class to develop their skills in data manipulation, statistical programming, and visualization design. All technical instruction will be from first principles, so no prior programming experience required. A laptop running Linux, MacOS or Windows must be brought to all class sessions. Pre-requisites: PPOL 501 or PPOL 531 or PPOL 552.

On the mathematical side, students will cover linear algebra with a special focus on matrix algebra (e.g. matrix notation, projections, determinants, inversions, and eigenvectors) and multivariate calculus up until constrained optimization. On the computer science side, students will be introduced to working at the command line and python programming, with introductions to data structures, data manipulation and programming paradigms. Students will work in notebooks and use Git/GitHub to submit coding assignments, developing literate programming and reproducible research skills they will use throughout the program.

ANLY 501: Intro to Data Analytics

Introduction of data science concepts (data collection, cleaning, filtering, pre-processing, modeling, knowledge extraction, actionable recommendations). The data science process and its connections to statistical techniques. Elements of database use and of SQL. Algorithms. Data exploration and elements of visualization. Ethical issues and ways to implement them. Examples applications include fraud detection, social networks, and spam filters, among others.

ANLY 502: Massive Data Fundamentals

In this course, students will learn the technology, business, science, and social implications of "big data" processing. In recent years there has been an explosion of tools, techniques, and technologies for working with massive data sets. Students will build real word systems, using stand-alone Hadoop/Spark environments running in VirtualBox on personal systems, and scalable clusters on Amazon Web Services. Topics: Big Data terminology, scaling from one computer to thousands, data storage and data privacy, Spark, data formats and data wrangling, text processing and web mining, streaming data, graph processing. Students will be provided Amazon Web accounts with allowances that are sufficient to cover the course work. Prerequisites: ANLY 501 or equivalent, working knowledge of Python and the Unix command line. Students need to own a laptop computer with at least 100GB of free disk space and 8GB of RAM.

ANLY 512: Statistical Learning

Basic concepts: Model accuracy, prediction accuracy, interpretability, supervised and unsupervised learning. Linear regression. Classification, logistic regression, linear discriminant analysis. Resampling methods, cross validation. Model selection, dimension reduction, and other high-dimensional considerations. Support vector machines. Unsupervised methods such as PCA and Clustering. If time permits: Splines, general additive models, tree-based methods. Prerequisites: Probabilistic Modeling and Statistical Computing (ANLY-511) or PPOL 560/561 or equivalent. Good knowledge of R or Python.

Electives (6 credits)

The remaining 6 credits can come from any course offered within either the McCourt School of Public Policy or the Graduate School’s MS-DS. Students with prior coursework equivalent to core courses may be allowed to test out of or waive these courses and take electives in their place. Note that the total number of credits required for graduation does not change for students who test out of core courses.

Please see below for a sample list of electives offered over the past academic year. This list is not exhaustive and additional courses can be found on the Registrar's Schedule of Classes. McCourt students also have the opportunity to take electives in other Georgetown graduate programs as well as through the Consortium of Universities of the Washington Metropolitan Area. Please contact Director of Academic Affairs Nirmala Fernandes at nf168@georgetown.edu for more information.

  • U.S. Domestic Economic Policy including courses such as:
    • PPOL 614: The Federal Budget in a Time of Madness
    • PPOL 623: National Economic Issues
    • PPOL 649: Macroeconomics
    • PPOL 758: Foreign Direct Investments in the US
    • PPOL 759: Getting People to Behave
  • International Economic Policy including courses such as:
    • PPOL 608: Asian Economic Development
    • PPOL 676: International Financial Institutions
    • PPOL 677: International Trade Policy & Negotiations
    • PPOL 734: Latin American Economic Development
  • Development Policy including courses such as:
    • PPOL 638: International Health
    • PPOL 647: International Social Development Policy
    • PPOL 681: BRICS & The Global Economy
    • PPOL 685: History and Theory of Development
    • PPOL 703: Political Economy of Foreign Aid
    • PPOL 780: Economic Complexity & Development
  • Political Strategy and Governance including courses such as:
    • PPOL 600: The Press & the Presidency
    • PPOL 612: Federalism & Intergovernmental Relations in the U.S.
    • PPOL 627: Identity Politics & Interest Groups
    • PPOL 632: Strategic Advocacy: Lobbying/Interest Groups
    • PPOL 657: Policy, Politics & the Media
  • Education Policy including courses such as:
    • PPOL 655: Education Productivity: Teachers & Technology Effects
    • PPOL 672: Topics: Post Secondary Education
    • PPOL 797: New Players in Education: Charter Schools
  • Environmental & Regulatory Policy including courses such as:
    • PPOL 613: Environmental and Natural Resources Economics
    • PPOL 636: Energy, Society & Politics in Developing Countries
    • PPOL 687: Nuclear Power, Climate Change, Clean Power
    • PPOL 711: Sustainable Development
  • Health Policy including courses such as:
    • PPOL 604: Health Care Quality: Recent Policy Issues
    • PPOL 642: Health Policy & Politics
    • PPOL 643: Health Care Access Demand Issues
    • PPOL 798: Politics & Policies of Addiction and Recovery
  • Homeland Security Policy including courses such as:
    • PPOL 688: Homeland Security
    • PPOL 692: Capacity Building/Counter-terrorism (previously Post Conflict Reconstruction)
    • PPOL 694: Cyber Conflict and National Security Policy
  • Management & Leadership such as:
    • PPOL 612: Federalism/Intergovernmental Relations
    • PPOL 633: Women and Leadership
    • PPOL 663: Public Leadership
    • PPOL 699: The Power & Influence of Philanthropy: Local, National, Global
    • PPOL 748: Negotiation
  • Methods including courses such as:
    • PPOL 622: Policy Analysis
    • PPOL 646: Data Visualization for Policy Analysis
    • PPOL 683: Spatial Data Modeling & Public Policy
    • PPOL 693: Applied Monitoring & Evaluation for Development Programs
    • PPOL 696: Survey Research Methods
    • PPOL 737: Game Theory
  • Public Management including courses such as:
    • PPOL 639: Strategic Planning & Public Policy
    • PPOL 663: Public Leadership
    • PPOL 680: Risk Management
    • PPOL 756: Contracting
    • PPOL 779: Agency Rulemaking & Adjudication: How Fed Govt Does Business
  • Social Policy including courses such as:
    • PPOL 604: Policy/Politics of Entitlements
    • PPOL 607: Child Development
    • PPOL 611: The War on Drugs: Causes, Consequences and Alternatives (formerly US Drug Policy & Its Consequences)
    • PPOL 659: Race, Faith & Politics
    • PPOL 664: Tax Policy
    • PPOL 745: U.S. Immigration Policy

Data Science in Action Seminars (0 credits)

In their first two semesters, students will attend non-credit Data Science in Action Seminars approximately once every month. These sessions will bring to Georgetown leading experts who are applying data science to difficult policy problems. Experts might include:

  • Jeff Chen, Chief Data Scientist at the Department of Commerce

  • Sallie Keller, Director of the Social & Decision Analytics Lab at Virginia Tech

  • Eric Mill, Open Data Developer at 18F

  • Julia Lane, Professor in NYU’s Center for Urban Science and Progress

  • Solomon Messing, Chief Data Scientist at the Pew Research Center