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

Program Dates

  • Session One: June 15, 2020 - August 2, 2020
  • Session Two: July 27, 2020 - September 13, 2020

Schedule Structure

The virtual program is an asynchronous, flipped classroom model, which enables students to watch lectures at their own pace and login for designated office hours with faculty and graduate teaching assistants from anywhere in the world.

Students can anticipate a commitment of approximately 15-20 hours per week. This weekly estimate is based on 10-12 hours of lecture (watched at your own pace), three-five hours for assignments and projects, one-two hours of live office hours with faculty and teaching assistants and one-two hours of live sessions of community resources. This part-time schedule makes the program more compatible with other summer activities, such as jobs or internships. 

The live office hours and sessions will accommodate our student time zones and occur at various times throughout the week. We are polling our admitted students to provide input on the live session schedule. You can anticipate a number of live office hours and activities to occur in the evening of Central Daylight Time (Chicago Time).

Review our FAQ for more information. 

Example Program Schedule

Week/Module Data Analytics  Programming in R UChicago and Program Resource Sessions
Week 1 Course Preview Course Preview 4.1 - Prgram 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)

4.2 - Virtual Chat with Program Administration Team

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

Week 3

1.3 - TBD 2.3 - Tidy data, data wrangling, and simple data cleaning

4.3 - Career Exploration Workshop

1.4 - Difference in means: RCTs (experimental ideal) 2.4 - Recoding, data transformation, and joins (plus more wrangling)

Week 4

1.5 - Bivariate regression: properties, testing, interpretation 2.5 - Data visualization and exploration (ggplot2, summarization)

4.4 - Graudate Programs Admissions in the US Workshop

1.6 - Multivariate regression: testing, interpretation, omitted variable bias 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)

4.5 - UChicago Harris Student and Alumni Virtual Chat/Panel

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)  
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