Curriculum

Gain the ability to understand and address global issues from multiple perspectives using data-analytical tools.

Participate in research projects by applying course concepts to real issues and incorporating feedback from faculty and team members.

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 2021 program offers two to three capstone project topics. Faculty prepare a list of project topics, and students vote to select their preferred topics. Topics are decided during the program. The skills gain in the project are transferable for further research in your area of interest.

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.

Past capstone projects

  • Conflict and Insurgent Learning (PDF, 4 pages): How do insurgents learn and adapt to their enemies?

  • Hate Crimes in the United States (PDF, 4 pages): What are the trends and implications of hate crime reporting?

  • Cyber Attacks and Stock Market Prices: Do company strategically decide on the timing to release the hack news?

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 21 - August 6, 2021
     
  • Session Two: August 2 - September 17, 2021

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 program allows you to engage with asynchronous lectures at your own pace and join synchronous office hours with faculty and graduate teaching assistants from anywhere in the world. Leverage a virtual discussion board for quick communication with peers and teaching assistants.

Academic lectures for Data Analytics and R Programming are delivered via weekly video modules (pre-recorded lectures). Students can watch and re-watch on their own time. The Capstone Research Project and Community Resources occur though live, synchronous sessions. 

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

This part-time format makes the program more compatible for those in full-time jobs or degree programs. The weekly time commitment varies per student based on their own learning pace.

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/Module Data Analytics  Programming in R UChicago and Program Resource Sessions
Week 1 Course Preview Course Preview  Program Welcome Orientation

Week 2

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

 Policy-in-Action Speakers

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

Week 3

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

 Career Exploration Workshops

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

Week 4

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

 Graduate Admissions Panel

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

Week 5

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

 Virtual Chats with UChicago student and alumni

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

Week 6

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 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