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.

Students who complete this specialization will be able to:

  • Write simple programs in R or 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 any University of Chicago graduate student. Harris students please indicate your intent to pursue this specialization using the Harris Specialization Declaration Form.

For specialization registration questions, please reach out to harrisregistration@uchicago.edu.

Planning for the Specialization

For information on which quarter(s) each course will be offered see the Harris Courses page and filter by specialization. For courses offered by other divisions the typical quarter(s) offered has been indicated.

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. Students should complete the formal signup process to indicate their intention to pursue the specialization.

Required courses

Students must complete a two-course sequence of Data and Programming I and II:

Students must complete one of the following courses:

  • PPHA 30545 Machine Learning for Public Policy
  • BUSN 41204 Machine Learning 
  • CMSC 35300 Mathematical Foundations of Machine Learning
  • MACS 33002 Introduction to Machine Learning
     

Elective courses

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

Specialization Contact

Dan Black, Interim Director

Peter Ganong, Specialization Director (on leave 23-24)

Peter

Associate Professor

Peter Ganong

Peter Ganong studies how households manage difficult financial circumstances such as unemployment and having an underwater mortgage.