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Cornell University

Cornell Brooks Public Policy

MS-DSP Curriculum

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Built for what comes next in policy leadership, the Brooks School’s MS in Data Science for Public Policy (MS-DSP) equips you to turn complex data into actionable insights for smarter, fairer decision-making. The curriculum blends advanced training in statistics, programming, and data modeling with a grounding in economics, ethics, and public policy analysis.

Our 41-credit, 12-month program emphasizes engaged learning, ensuring students apply technical and analytical skills in real-world policy contexts. Faculty expertise, a collaborative cohort experience, and connections with practitioners prepare graduates for impactful careers across government, nonprofits, and industry.

Course Load

Summer Session: 6 credits
Fall Semester: 16 credits
Winter Session: 3 credits
Spring Semester: 16 credits
Total: 41 Credits

Topical Areas

    • Statistics and data analytics
    • Data management and programming
    • Data modeling and machine learning
    • Microeconomics and political analysis
    • Managing and leading organizations
  • Data visualization and communication for policy
  • Data ethics, equity, and emerging technology

Engaged Learning Capstone

In the culminating capstone, students apply technical, analytical, and policy skills to a project with an external policy partner organization. These projects address strategic priorities in data science and public policy and are guided by faculty and executives-in-residence.

Sample Semester-by-Semester Curriculum

The sequence below illustrates a typical course plan. Specific courses, schedules, and sequencing may vary based on course offerings and other factors.

Summer Semester (6 credits)

Foundations
  • PUBPOL 5575 Statistics for Public Policy
  • PUBPOL 5799 Data Management and Programming

Fall Semester (16 credits)

Technical Skills & Policy Foundations
  • PUBPOL 5008 Designing Your Career in Public Policy, Data Science and Sustainability
  • PUBPOL 5210 Intermediate Microeconomics for Public Affairs
  • PUBPOL 5390 Foundations of Machine Learning for Public Policy
  • PUBPOL 5605 Political Analysis and the Policy Process
  • PUBPOL 5610 Causal Inference and Data Analysis for Public Policy
  • PUBPOL 5840 Data Visualization for Public Policy
  • Potential Elective(s)

Winter Semester (3 credits)

Advanced Data Science Modeling
  • PUBPOL 5391 Unstructured Data Science Modeling

Spring Semester (16 credits)

Leadership & Application
  • PUBPOL 5414 Project Management or PUBPOL 5665 Managing and Leading Organizations
  • PUBPOL 5725 Ethics in Data, Data Science, and AI for Public Policy
  • PUBPOL 5805 Communication for Public Policy
  • PUBPOL 5880 Brooks Engaged Learning Capstone
  • Potential Elective(s)

Program Requirements

View MS in Data Science for Public Policy (MS-DSP) program requirements and policies in the University Catalog.