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 emphasize 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 563: Data Visualization (3 credits)
PPOL 564: Data Science I: Foundations (3 credits)
PPOL 565: Data Science II: Applied Statistical Learning (3 credits)
PPOL 566: Data Science III: Advanced Modeling Techniques (3 credits)
PPOL 567: Massive Data Fundamentals (3 credits)
Ethics and Law (1.5 credits)
- PPOL 568: Data Ethics (1.5 credits)
Communication (1.5 credits)
- PPOL 569: Data Communication (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 511: COMPARATIVE PUBLIC POLICY PROCESS
This course provides a cross-national perspective on the institutions and processes of public policy-making. The first section of the course examines a series of theoretical perspectives for analyzing constraints on and opportunities for policymaking, including political culture, globalization, and feedbacks from past policy choices. The central section of the course focuses on political institutions as venues and vehicles for policymaking. The final section of the course looks at case studies of specific policy sectors, such as pensions, health care, and the environment. Throughout the course, there will be a balance between general and theoretical materials and a more intensive examination of a small number of countries.
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 generic function performed in all sectors (public, nonprofit, and private) but participants are encouraged to focus on what factors make public management distinctive and more challenging. Some of the specific topics covered include: forms and styles of decision-making; the role of front-line operators and middle managers; organizational culture; the organizational environment (including Congress, the President, and the courts); managing people; management reform efforts (budget reform; privatization and contracting out; performance management). Case studies are used to explore specific challenges and scenarios.
PPOL 515: COMPARATIVE PUBLIC MANAGEMENT
A key component in the study of public policy is the ability to work through how it gets put into action. Successful policy implementation requires more than technical policy analysis; it requires a critical understanding of the underpinnings of good management and governance. This course uses theoretical concepts, case study materials and simulations to introduce students to the difficulties of public management in the international realm. We will focus specifically on the challenges facing less developed countries as well as the options and resources available to effectively implement policies in those countries. Through class discussion, cases and a client-based management project, students will apply concepts in comparative public management to actual situations.
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, inference and identification. In studying inference, we will cover probability distributions, random variables, and hypothesis testing. In studying identification, we 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. We will also introduce maximum likelihood estimation by covering logit and probit models. This course is taught in R.
PPOL 561: ACCELERATED STATISTICS FOR PUBLIC POLICY 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 with a focus on identification of causal effects. Topics covered include: random assignment experiments, non-experimental methods such as regression discontinuity designs, instrumental variables, panel data methods including difference-in-difference models, and propensity score matching. The course will also cover additional maximum likelihood estimation of models with limited dependent variables (such as tobit models) 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 560.
Civic Data Science
PPOL 563: 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.
PPOL 564: DATA SCIENCE I: FOUNDATIONS
This first course in the core data science sequence introduces students to the programming and mathematical concepts that underpin statistical learning. The aim of the course is to provide students with the foundations necessary to grasp the concepts and algorithms encountered in Data Science II and III. Students will cover probability theory with an emphasis on simulation, Bayes rule, and MCMC sampling; linear algebra with a focus on data decompositions and dimension reduction; and multivariate calculus with an emphasis on optimization algorithms, specifically gradient descent. Throughout the course, students will be introduced to the fundamentals of programming in Python and will learn about data structures, data manipulation, and basic data management. Students will work in Jupyter notebooks and use Git/GitHub to submit coding assignments, developing literate programming and reproducible research skills they will use throughout the program. Formerly Foundations of Data Science.
PPOL 565: DATA SCIENCE II: APPLIED STATISTICAL LEARNING
This second course in the core data science sequence offers students an applied understanding of three key data science skills: data collection, data wrangling, and machine/statistical learning. Students will learn to gather raw data (using web scraping techniques and APIs); clean, structure, and manipulate data in a variety of formats; effectively explore and visualize data; and analyze datasets using a variety of machine learning models including regression, naive Bayes, K-nearest neighbors, decision trees and random forests, and support vector machines. Throughout the course, emphasis will be placed on effective visualization, model refinement and validation, and ethics. Students will engage with a number of policy-relevant data case studies throughout the course and will work on a policy-focused data science project. Prerequisite: PPOL 564. Formerly ANLY 501.
PPOL 566: DATA SCIENCE III: ADVANCED MODELING TECHNIQUES
This final course in the core data science sequence focuses on unsupervised learning techniques, natural language processing, and network analysis. The course builds off of the modeling concepts covered in Data Science II by teaching students how to effectively explore, model, and predict with unstructured data, such as text or data streams like those encountered in social media. Students will engage with a number of policy-relevant data case studies throughout the course and will work on a policy-focused data science project in which to apply their statistical learning toolkit. Prerequisite: PPOL 565.
PPOL 567: Massive Data Fundamentals
Today's data scientists are commonly faced with huge data sets (Big Data) that may arrive at fantastic rates and in a broad variety of formats. This core course addresses the resulting challenges to data professionals. The course will introduce students to the advantages and limitations of distributed computing and to methods of assessing its impact. Techniques for parallel processing (MapReduce) and their implementation (Hadoop & Spark) will be covered, as well as techniques for accessing unstructured data and for handling streaming data. These techniques will be applied to real-world examples, using clusters of computational cores and cloud computing.
Ethics and Law
PPOL 568: DATA ETHICS (1.5 credits)
In learning a rapidly emerging field with many benefits, it is easy to glaze over the many possible negative repercussions of data science. This course will focus on generating student awareness of the many critical and unanswered questions that require thoughtful consideration. Some pressing issues include: the ways in which using data can subtly exacerbate existing systemic prejudices, such as through implicit algorithmic bias; the challenges of making data open without revealing more than intended; the progressive corporatization of data and data ownership issues; as well as dealing with privacy and data security issues in the public sector.
PPOL 569: DATA COMMUNICATION (1.5 credits)
Clearly communicating problems, ideas, data, analysis approaches, results, and recommendations for action are vital for career success in technology and science. Strong technical writing is clear and unambiguous, easy to read, and concise. This course improves students’ writing, presentation, and critique skills. They will learn to communicate material to technical and non-technical audiences. Students will learn to write strongly by improving text clarity, simplicity, conciseness, and incorporating high-quality graphics (LaTex will be used for paper preparation). Students will learn to craft oral presentations that are clear, easy to follow, informative and compelling, and will develop delivery skills that improve comprehension audibility, comfort, and audience engagement.
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 firstname.lastname@example.org 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