Specialization in Data Analytics

With increased digital access to data and the development of powerful, but inexpensive, computing, in the 21st century the formulation and evaluation of public policy is more and more reliant on the analysis of data. This specialization seeks to prepare students for careers where data analysis plays a central role. This specialization is designed for beginners without a prior background in coding and is a marker of courses passed rather than a competency determination.

Students who complete this specialization will be able to:

• Write simple programs in Python
• Learn modern tools for data management, analysis, and presentation, including github, matplotlib, pandas, R, and SQL
• Construct and clean data sets from disparate sources and understand how to summarize and visualize modern data sets
• Use modern, computationally intensive methods to analyze data for the evaluation of policy
• The specialization's menu of electives is designed to allow students to increase their exposure to analytical methods used in the evaluation of public policy. 

Harris specializations function as areas of focus within the degree. Specializations assume knowledge of the 6-course Harris Core and build upon that foundation with coursework in specific policy areas or technical skills.

Students in the Master of Science in Computational Analysis and Public Policy (MSCAPP) program may not earn this specialization.

Application Process

The specialization is open to Harris Public Policy graduate students only. Please indicate your intent to pursue this specialization using the Harris Specialization Declaration Form.

Planning for the Specialization

For information on which quarter(s) each course will be offered see the Harris Courses page and filter by specialization.

Specialization Requirements

The specialization requires completion of three required courses, along with one additional course (four courses total). Students must achieve at least a B- grade in each course. No specialization course may be taken on a pass/fail grading basis. No substitutions will be allowed for required courses, and no non-Harris substitutions will be considered for electives.

For course descriptions, please visit our courses webpage.

Required courses

Students must complete a three-course sequence of:

• PPHA 30535 or PPHA 30537 Data and Programming for Public Policy I
• PPHA 30536 or PPHA 30538 Data and Programming for Public Policy II
• PPHA 30545 Machine Learning for Public Policy

Elective courses

Students must complete one of the following courses to fulfill the four-course requirement:

• PPHA 34600 Program Evaluation 
• PPHA 38829 Artificial Intelligence for Public Policy
• PPHA 42000 Applied Econometrics I 
• PPHA 42100 Applied Econometrics II 
• PPHA 60000 Policy Labs (with permission of the Specialization Director)
• CAPP 30300 / PPHA 30581 Data Science Clinic

Specialization Contact

Peter Ganong, Specialization Director