Studying Incarceration with Data

Description from DukeHub

This course introduces students to data-informed approaches for examining the social, structural, and institutional factors that shape the experiences and outcomes of individuals who have been incarcerated. Students will learn how data is used to understand patterns of incarceration, as they critically engage with quantitative research and produce statistical analyses from real-world data. Topics in the course include exploratory data analysis, data visualization, statistical inference, and regression modeling using the R computing language. The course also emphasizes data ethics and communicating data-informed results to diverse audiences. No statistics or computing background is necessary.

Skills

• data and statisticsdescription: "quantitative" and 7 more
• lab workhas a lab component

AI-resilient: 2 of 5 2/5

• ✓Yes: Physical, lab or field work · "laboratory"
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "ethics"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

More in STA

• Data Analysis and Statistical Inference STA 101L
• Introduction to Data Science and Statistical Thinking STA 199CCL
• Regression Analysis STA 210L
• Regression Analysis: Theory and Applications STA 221L
• Statistical Computing STA 323L
• Statistical Methods for Sports Analytics STA 345

All STA courses · All of Undergraduate