Master of Science in Public Policy and Management: Data Analytics Track
Evidence-Based Solutions for the World's Problems
This track is right for you if:
- You want the most quantitative policy degree we offer
- You believe that artificial intelligence and machine learning innovation will benefit the public good
- You have some familiarity with pre-calculus and statistics
Key Program Information
Everything you need to know about the Data Analytics Track of the Master of Science in Public Policy and Management program.
Application Deadlines
- Round 1: December 1, 2026
- Round 2: January 10, 2027
- Round 3: February 1, 2027
Cost of Attendance
Please visit CMU's Student Financial Services website.
Public Policy as "A People Problem"
Professor Rayid Ghani break down how the Data Analytics Track prepares students to use data, evidence and analytics to drive real-world impact.
Curt Williams chose the Data Analytics track to augment his statistics background, as well as add Python and R to his repertoire.
Public Policy and Management: Data Analytics Curriculum
For detailed curriculum information, please visit the Master of Science in Public Policy and Management Student Handbook.
- Policy and Politics: American Political Institutions (90-714) or An International Perspective (90-713)
- Applied Economic Analysis (90-710)
- Accelerated Statistics (90-711)
- Organizational Design and Implementation (94-700)
- Writing for Public Policy (90-717)
- Strategic Presentation Skills (90-718)
- Database Management for Policy Analytics (90-838)
- Optimization and Decision Modeling for Analytics (90-755)
- Accounting and Finance Analytics (95-719)
- Strategic Communication for Public Policy and Management (90-877)
- Python Programming (90-819)
- Exploratory Data Analysis and Visualization in Python (90-800)
- Applied Econometrics I (94-834)
- Applied Econometrics II (94-835)
- From Data to Action (94-867)
- Public Policy Capstone Project
PLUS
- Required Summer Internship
For class, syllabi, and course information, please visit the Heinz College course catalog.
- Education Finance and Policy (90-817)
- Policy in a Global Economy (90-860)
- Elective Politics and Policy-Making (90-754)
- Generative AI: Applications, Implications and Governance (94-816)
- Critical Analysis of Policy Research (90-822)
- Working in the Policy Ecosystem (90-897)
- Using R for Policy Data Analysis (90-872)
- Machine Learning for Public Policy Lab (94-889)
- Unstructured Data Analytics (94-775)
- Data Science and Big Data (95-885)
- Fundamentals of Operationalizing AI (94-879)
- Python Programming (90-812)
- Applied Econometrics (94-834and94-835)
- Telling Stories with Data (94-870)
- Policy Innovation Lab (90-783)
- Design Thinking (94-866)
- Behavioral Economics (90-880)
- Program Evaluation (90-823)
- Health Economics (94-705)
- Evidence-Based Management (94-814)
*Elective availability may differ by pathway/location; students may also enroll in graduate-level courses from other Heinz College programs as well as other departments across Carnegie Mellon University’s campus, with approval.
For class, syllabi, and faculty information, please visit the Heinz College course catalog.
Students can elect an optional concentration in one of the following policy areas, or define their own:
- AI Management
- Energy and Environmental Policy
- Health Policy
- International Policy
- Nonprofit and Public Management
- Public Interest Technology
- Social Policy
- Technology Governance, Cybersecurity, and Privacy Policy
- Urban & Regional Economic Development
- U.S. Politics and Policy Making
Sample Public Policy and Management: Data Analytics Track Class Schedule
- At Carnegie Mellon University, courses are counted in units instead of credits.
- Three units are roughly equivalent to one credit at many of our peer institutions.
- A semester-long course is 12 units, while a half-semester class (called a mini) is six units.
The following schedules are intended to be examples of the structure of the semester. Courses may not always be offered in this fashion.
Sample Schedule: Fall
| Fall, Mini 1 | Fall, Mini 2 |
| Applied Economic Analysis | Applied Economic Analysis |
| Accelerated Statistics | Business Writing |
| Database Management for Policy Analytics | Database Management for Policy Analytics |
| Data-Focused Python | Exploratory Data and Visualization in Python |
| Total units: 24 | Total units: 24 |
Sample Schedule: Spring
| Spring, Mini 3 | Spring, Mini 4 |
| Policy and Politics | Policy and Politics |
| Organization and Decision Modeling for Analytics | Organization and Decision Modeling for Analytics |
| Applied Econometrics I | Applied Econometrics II |
| Machine Learning Foundations with Python | Machine Learning Foundations with Python |
| Total units: 24 | Total units: 24 |
