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.
Fall 2027 Public Policy and Management: Data Analytics Curriculum
- Policy and Politics: American Political Institutions (90-714) or An International Perspective (90-713)
- Policy Analysis
- Applied Economic Analysis (90-710) or Intermediate Economic Analysis (90-709)
- Accelerated Statistics (90-777)
- Organizational Design and Implementation (94-700)
- Strategic Communication for Public Policy and Management
- Python Programming II (90-819) or Data Focused Python (95-888)
- Exploratory Data Analysis and Visualization in Python (90-800)
- Machine Learning Foundations with Python (90-803)
- Applied Econometrics I (94-834)
- Applied Econometrics II (94-835)
- From Data to Action (94-867)
- Database Management for Policy Analytics* (90-838)
- Optimization and Decision Modeling for Analytics* (90-755)
- Accounting and Finance Analytics* (95-719)
- Public Policy Capstone Project
- PPM Professional Seminar
Data Analytics students will choose from targeted electives to gain skills and experience in the following content areas:
- Policy Domains
- Lab-Based Experiential Learning
- Artificial Intelligence
For class, syllabi, and faculty information, please visit the Heinz College course catalog. Fall 2026 current students, please visit the Master of Science in Public Policy and Management Student Handbook.
*These classes are the Data Analytics track versions of the Public Policy core.
- Telling Stories with Data (94-870)
- Policy Innovation Lab (90-783)
- Critical Analysis of Policy Research (90-822)
- Program Evaluation (90-823)
- Negotiation (94-800)
- Federal Budget Policy (90-894)
- Poverty Reduction and Social Policies in the U.S. (90-704)
- Local and Regional Economic Development (90-743)
- Systems Analysis: Environmental Policy (90-798)
- Python Programming (90-812)
- Machine Learning for Public Policy Lab (94-989)
- Unstructured Data Analytics (95-865)
- Data Science and Big Data (95-885)
- Responsible AI: Principles, Policies, Practices (94-885)
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.
- Complete 192 units over two years, including a required summer internship and capstone experience.
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 |
Intermediate Economic Analysis | |
Policy & Politics: An International Perspective | |
PPM Professional Seminar | |
| Python Programming II | Exploratory Data and Visualization in Python |
| Accelerated Statistics | Elective: Demystifying AI |
Sample Schedule: Spring
| Spring, Mini 1 | Spring, Mini 2 |
Policy Analysis | |
Optimization and Decision Modeling for Analytics | |
Machine Learning Foundations with Python | |
| Applied Econometrics I | Applied Econometrics II |
