Master of Science in Public Policy and Management 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)
• Statistical Reasoning (90-707) or Statistical Reasoning with R (90-711) or Accelerated Statistics (90-777)
• Optimization and Decision Modeling for Policy (90-722)
• Organizational Design and Implementation (94-700)
• Financial Statements and Analysis (90-723)
• Strategic Communication for Public Policy and Management
• Introduction to Database Management (90-728)
• Public Policy Capstone Project
• PPM Professional Seminar

In Fall 2027 and beyond, public policy students will choose from targeted electives to gain skills and experience in the following areas: 

• Policy Domains
• Policy Methods*
• AI for Policy Selective
• Lab-Based Experiential Learning
• Management*

Students pursuing the Data Analytics and Washington, DC tracks may automatically fulfill some of these requirements through their course plan. 

*Policy methods and Management are not applicable to students pursuing the Data Analytics track. 

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.

Data Analytics Coursework is taken in addition to the Core coursework listed above.

• 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)

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.

DC Coursework is taken in addition to the Core coursework listed above.

• Working the Policy Ecosystem (90-897)
• Policy in Action I/II (90-879)
• Program Evaluation (90-823)
• Leadership/Management Workshop: How Washington Works (90-855)
• Federal Budget Policy (90-894)
• Capstone (Systems Synthesis): Project Planning 

PLUS

• Policy Analysis Capstone Project

PLUS

• Heinz Policy Fellowship at a Washington, DC-based agency or organization (Year Two, ~30 hours/four days per week)

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.

• 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: Fundamentals, Policy, and Implementation (94-885)
• Geographic Information Systems (94-802)
• Behavioral Economics in Public Policy
• U.S. Energy and Climate Policy
• Policy in a Global Economy
• Racial & Gender Impact on U.S. Policies
• Demystifying AI
• Lean Innovation Lab
• Policy Innovation Lab
• AI and Democracy (94-816)

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 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.

Policy Methods*

In their policy methods courses, students will be encouraged to explore the methods that underpin today's policy process. These courses provide students with the tools necessary to conceptualize, implement, and evaluate policy, critical elements to secure real policy change. 

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. 

Management*

Management courses will prepare students to do more than just innovate policy - they'll be prepared to lead it, and the teams that support it, in a fast-changing world. 

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)

*Students pursuing the Data Analytics and Washington, DC tracks may automatically fulfill some of these requirements through their course plan.