Curriculum

Gain the ability to understand and address global issues from multiple perspectives using data-analytical tools. There are three academic components to this program: Data Analytics, Introduction to R Programming and a Capstone Research Project. 

Data Analytics in Public Policy

This course provides an introduction to the statistical foundations, tools, and methods employed by public policy researchers. Explore the fundamental problem of causal inference and learn how to use data, research design, and statistical modeling to navigate around this problem.

Introduction to Programming in R

This is an introductory course in programming and data analysis for students with no prior coding experience. The course has three learning outcomes: introduce students to the tools required to write and share code; translate self-contained questions into R programs; and learn how to retrieve, clean, visualize, and analyze data.

Capstone Research Project

In the capstone research project, you will collaborate with Austin Wright, Assistant Professor and DPSS Faculty Director, and a group of peers on a real-world problem and design a policy recommendation. 

You will harness the skills of research design, policy analysis, and team collaboration to conduct a research project using open-source or faculty-provided datasets. There are elements of data collection, analysis, and visualization, and result in a policy memo. 

Learning outcomes and the policy memo become a portfolio piece that highlights your academic readiness for graduate program admissions or for applications for internships or jobs. The skills gain in the project are transferable for further research in your area of interest.

The program offers two-four capstone project topics. Faculty prepare a list of project topics, based on student interest, and students vote to select their preferred topics. Students learn the topics during the program.

Before the program begins, we invite admitted students to share their suggested policy topics - a benefit of applying early! This will help shape which projects the faculty choose for the program.

Hear Professor Wright talk about the capstone research project.

Past capstone projects

See how alumni used these skills after the program.


Credential Award

Participants will receive two documents, issued electronically, upon completion of the credential program:

  • UChicago Transcript with pass/fail marks for each course
     
  • Certificate of Credential Completion, issued by Harris Public Policy, with a letter grade for each course

Headshot of Manish Muthukrishnan
Manish Muthukrishnan

"I've always been in public policy, but the data element and how you can incorporate that into public policy is something that DPSS has really given me. Even after this, I want to go back and continue learning."

Manish Muthukrishnan, 2019 DPSS Participant, BC Economics and Marketing, University Of Canterbury

Instructors

Austin Wright
Faculty Director for the Data and Policy Summer Scholar Program Austin Wright

Austin Wright

Austin Wright is the Faculty Director for the Data and Policy Summer Scholar Program, ensuring the holistic curriculum is designed and taught to meet student needs in the UChicago way. Wright is an Assistant Professor at the Harris School of Public Policy, and faculty affiliate of The Pearson Institute for the Study and Resolution of Global Conflicts at the University of Chicago.


Schedule and Format

Program Dates

  • Session One: June 13 - July 31, 2022
  • Session Two: July 25 - September 10, 2022

The Data and Policy Summer Scholar Program occurs twice during the summer. The two virtual sessions are identical. Participants can apply to the session that best fits their schedule.

View the application requirements and application process. 

Virtual Format

The virtual format allows students to engage with asynchronous (pre-recorded) lectures for Data Analytics and R Programming delivered via weekly video modules. Students can watch and re-watch on their own time from anywhere in the world. 

Students receive daily support by joining synchronous office hours with faculty and graduate teaching assistants or chatting in the virtual discussion board. Live office hours are offered approximately 15 hours per week at various times to accommodate our global and working students.

The Capstone Research Project, in the last two weeks of the program, includes live lectures with faculty, office hours with teaching assistance, and collaboration with a group of peers. 

Community Resources occur though live, synchronous sessions. These are approximately two-three hours of live lectures or workshops per week. 

Read our blog post on the Benefits of the Virtual Format

Time Commitment

Anticipate a commitment of approximately 10-15 hours per week. This weekly estimate is based on: 

  • 1-2 hours of live office hours with faculty, teaching assistants and study group
     
  • 2-3 hours of live sessions of UChicago community resources
     
  • 3-5 hours of assignments and projects
     
  • 4-6 hours of lectures, watched and re-watched at your own pace

The weekly time commitment varies per student based on their own learning pace. This part-time format makes the program a compatible supplement your part-time or full-time academic study or internship/employment.  

Office Hours

Live office hours accommodate various time zones and occur multiple times throughout the week. Live office hours are held in the mornings and evenings of Central Daylight Time (Chicago Time, UTC-5).

Example Schedule

Week  Data Analytics   Programming in R UChicago and Program Resource Sessions

Week 1

1.1 - Foundations of Causal Inference for Public Policy 2.1 - Intro to R and RStudio (working dirs, projects, panes, R basics, etc)

 Program Orientation

1.2 - Mean, Variance, Random Variables, and Samples 2.2 - Intro to tidyverse, fundamentals of data, basic visualization;

Week 2

1.3 - Difference in means: RCTs (experimental ideal) 2.3 - Tidy data, data wrangling, and simple data cleaning

 Policy-in-Action Speakers

1.4 - Bivariate regression: properties, testing, interpretation 2.4 - Recoding, data transformation, and joins (plus more wrangling)

Week 3

1.5 - Multivariate regression: testing, interpretation, omitted variable bias 2.5 - Data visualization and exploration (ggplot2, summarization)

 Career Exploration Workshops

1.6 - Binary outcomes and functional form 2.6 - APIs and policy applications (working with Census data)

Week 4

1.7 - Panel data designs: fixed effects, first differences 2.7 - Programming concepts (for loops, functions, control flow)

 Networking with UChicago students and alumni

1.8 - Difference in Differences Design 2.8 - Causal inference stats in R (lm, sample, distributions, stargazer)

Week 5

1.9 - Regression discontinuity designs 2.9 - Introduction to spatial data (sf, tmap, ggmap)  Writing in Policy Workshops
1.10 - Instrumental variables 2.10 - Literate programming (RMarkdown, code syntax), GitHub

Week 6 & 7

Capstone Research Project
3.1 - Capstone Project Kick-off Meeting
3.2 - Policy Memo Writing Workshop
3.3 - Capstone Working Group
3.4 - Capstone Mid-cycle Check Meeting
3.5 - Capstone Presentation Summit