Curriculum
The Master of Science in Data Science for Public Policy (DSPP) is a 39-credit degree program that combines the McCourt School’s renowned policy analysis curriculum with state-of-the-art, master’s-level data science courses. The program is designated STEM-eligible.
Skill-based learning
In the DSPP program, you’ll develop sought-after technical and quantitative skills with deep policy expertise, empowering you to use data science to build a better world. Hands-on learning is a cornerstone of the McCourt School experience, offered through your internship and coursework. Our courses strengthen your abilities in research, data analysis and data science while increasing your proficiency in Python and R.
Skills you’ll gain
Data visualization / Statistics / Understanding massive data fundamentals
Course sequence
Students complete the program full-time in two years or 21 months by following this course sequence. Please note that all DSPP students must also fulfill an internship requirement.
| Semester | Course | Credits |
|---|---|---|
| Fall | McCourt Foundations | 0 |
| – | PPOL 5004 — Intermediate Microeconomics I | 3 |
| – | PPOL 5006 / PPOL 5007 — The Politics of Policy-Making/Comparative Politics of Policy-Making | 3 |
| – | PPOL 5200 — Accelerated Stats for Public Policy I | 3 |
| – | PPOL 5203 — Data Science I: Foundations | 3 |
| Spring | PPOL 5201 — Accelerated Stats for Public Policy II | 3 |
| – | PPOL 5204 — Data Science II: Applied Statistical Learning | 3 |
| – | PPOL 5207 — Data Ethics | 1.5 |
| – | PPOL 5208 — Communication for Data Science | 1.5 |
| Semester | Course | Credits |
|---|---|---|
| Fall | PPOL 5202 — Data Visualization | 3 |
| – | PPOL 5205 — Data Science III: Advanced Modeling Techniques | 3 |
| – | Elective | 3 |
| Spring | PPOL 5008/5009 — Public Management/Mgmt. & Implementation in Dev. Countries | 3 |
| – | PPOL 5206 — Massive Data Fundamentals (or PPOL 6810, which is offered in Year Two: Fall Semester, then students can take an elective) | 3 |
| – | Elective | 3 |
Required courses
Core courses
The core courses emphasize analytical skills and core knowledge for designing and managing sound public policy.
Quantitative Social Sciences (6 credits)
- PPOL 5200 Accelerated Statistics for Public Policy I (3 credits)
- PPOL 5201 Accelerated Statistics for Public Policy II (3 credits)
Foundations of Public Policy (9 credits)
- PPOL 5004 Intermediate Microeconomics I (3 credits)
- PPOL 5006 The Politics of Policy-Making; or PPOL 5007 Comparative Politics of Policy-Making (3 credits)
- PPOL 5008 Public Management; or PPOL 5009 Mgmt. & Implementation in Dev. Countries (3 credits)
Civic Data Science (15 credits)
- PPOL 5202 Data Visualization (3 credits)
- PPOL 5203 Data Science I: Foundations (3 credits)
- PPOL 5204 Data Science II: Applied Statistical Learning (3 credits)
- PPOL 5205 Data Science III: Advanced Modeling Techniques (3 credits)
- PPOL 5206 Massive Data Fundamentals (3 credits) or PPOL 6810 Relational Database Sys & SQL (3 credits)
Ethics and Law (1.5 credits)
- PPOL 5207 Data Ethics (1.5 credits)
Communication (1.5 credits)
- PPOL 5208 Communication for Data Science (1.5 credits)
McCourt Foundations
McCourt Foundations is a mandatory course for all full-time and part-time evening MPP students that occurs prior to the start of their first fall semester. The course is designed to facilitate the transition to graduate school for incoming students by developing core leadership and communication skills and fostering equity-centered policy work. Led by McCourt faculty, staff and Leadership Fellows, the course builds the foundational skills and confidence necessary to design, implement and measure the effectiveness of policy, while introducing them to their new community. Students must attend all five days from 9 a.m. to 5 p.m., with optional evening activities, and any student missing sessions will need to make up the course the following year.
Internship requirement
All DSPP students are required to complete a formal internship with a minimum of 120 work hours. This can be completed at any point during the program. In the internship, you will:
- Increase your proficiency in specific public policy disciplines such as management, statistics, economics, data science, politics and policy-making.
- Apply quantitative, economic, data science and policy analysis concepts and theories to real-world decision-making.
- Develop and improve policy-making skills in communication, quantitative or qualitative reasoning, data or policy analysis and teamwork.
Students can waive the internship requirement based on prior work or internship experience. For more questions, please contact Assistant Director of Academic Affairs Alora Hasson.
Electives
DSPP students take a minimum of six credits of elective coursework from any course offered within the McCourt School or the Graduate School’s Master of Science in Data Science & Analytics program. With permission, you can take electives in other Georgetown graduate programs and through the Consortium of Universities of the Washington Metropolitan Area. Please contact Assistant Dean for Academic Affairs Nirmala Fernandes at nf168@georgetown.edu for more information.
If you have prior coursework equivalent to core courses, you may be allowed to test out of or waive these courses and take electives in their place. The total number of credits required for graduation does not change for students who test out of core courses.
You may take additional electives, subject to the following Graduate School rules.
- Graduating students must have a 3.5 GPA or higher to be eligible to take additional credits in their final semester.
- Additional credits will be charged per credit to the student’s account.
You may also audit courses as per Graduate School policies.
Please note that this is just a sample of recent elective offerings. This list is not exhaustive and availability of courses varies.
McCourt’s Foundational skill set
McCourt’s Curriculum Innovation Committee has developed a set of core competencies based on extensive research and outreach for all of our degree programs. By integrating these core competencies across our academic offerings, all McCourt students graduate with these foundational skills:
- Collaboration
- Critical Thinking
- Economic Analysis
- Engaging with Bias
- Ethical Leadership and Management
- Evaluation
- Policy Analysis
- Political Analysis
- Quantitative Reasoning
- Strategic Communication
Data Science in Action seminars
The Data Science for Public Policy program offers extensive extracurricular learning opportunities, such as our Data Science in Action seminars, which are held approximately once per month. These sessions allow students to interact with leading experts applying data science to policy challenges. Past speakers include:
- Katie Kaufman, Senior Data Architect, Palantir
- Elaine Sedenberg, Manager of Global Affairs at Meta
- Dan Rosenbaum, Supervisory Economist, IRS and former Senior Director Analytics for the Detroit Pistons
- Maximilian Hell, Senior Data Scientist, Code for America
- Ben Jaques-Leslie, Program Evaluator, US Office of Personnel Management
- Arun Gupta, Noble Reach
- Pedro Chavez, tech policy leader with leadership experience at Google, Microsoft, LinkedIn and the office of U.S. Senator John McCain.
- Jennifer Atala, Senior Director for Artificial Intelligence at the Department of Homeland Security
- Eric Dunford, Meta
- Victoria Houed, Director of AI Policy and Strategy, Department of Commerce
- Jessica Smith, Committee on House Administration —Artificial Intelligence Strategy
- Amen Ra Mashariki, Ph.D., Director of AI and Data Strategies, Bezos Earth Fund
- Nick Nigro, Founder and CEO, Atlas Public Policy
- Martelle Esposito, Partnerships and Evaluation Manager, Nava
Request more information
The need for policy makers who can use data to drive change is ever growing. Join us in the Master of Science in Data Science for Public Policy and hone your skills to expertly apply data science to policy making.
Thank you for your interest in our program. Please complete this form and we‘ll contact you with more information.