Master of Science in Public Policy and Management: Data Analytics Track

• 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 Policy

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

Learn More About Our Policy Concentrations

To help students further develop policy expertise in their domain of choice, they will choose a number of selectives - courses from a curated list that add policy depth and breadth in areas critical for today's leaders.

Policy Domains

Students are encouraged to dive deeper into their domain of choice with a variety of policy domains selectives. These curated courses provide students with the necessary depth to be effective in their policymaking across key areas.

Lab-Based Experiential Learning

Students will gain additional hands-on experience beyond their capstone project through a lab-based experiential learning selective. These courses will provide students with opportunities to work on real policy challenges alongside faculty thought leaders, sharpening their real-world experience even before graduation.

Artificial Intelligence for Policy

Students will have the opportunity to take courses exploring the intersection between public policy and artificial intelligence, gaining the ability to understand, evaluate, and responsibly apply AI within policy, regulatory, and organizational contexts across sectors. To fulfill the AI selectives requirement*, students may take one or more of the following courses:

• Demystifying AI (94-703)
• Introduction to Artificial Intelligence (95-891)
• Fundamentals of Operationalizing AI: Mastering AI Lifecycle from Theory to Practice (94-879)
• Responsible AI: Principles, Policies, Practices (94-885)
• AI, Ethics and Humanity (94-830)