Studying Social Inequality with Data Science
  • Syllabus
  • Team
  • Schedule
  • Office Hours
  • Ed Discussion
  • Forms
  1. Schedule
  • Home
  • Topics
    • Welcome!
    • Population Sampling
    • Working with Data
      • R and RStudio
      • Visualization
      • Data transformation
      • Weights
      • Statistical Learning
      • Learning Exercise
      • Sample Splitting
    • Describing Inequality
      • Economic opportunity
      • Race
      • Gender
      • Class
    • Asking Questions
    • Reducing Inequality
      • Moral Arugments
      • Causal Interventions
  • Assignments
    • Problem Sets
      • Problem Set 0
      • Problem Set 1
      • Problem Set 2
      • Problem Set 3
      • Problem Set 4
      • Problem Set 5
    • Project
      • Discussion 3-21

On this page

  • Assignments
  • Meetings

Schedule

Assignments

Some assignments have associated readings, which are listed in the reading column. Due dates are tentative for assignments that are not yet released.

Due date Assignment Reading
Wed Jan 24 at 5pm Problem Set 0
Wed Jan 31 at 5pm Problem Set 1 Cheng 2021
Wed Feb 7 at 5pm Peer Review 1
Wed Feb 14 at 5pm Problem Set 2 England et al. 2020
Wed Feb 21 at 5pm Peer Review 2
February break
Wed Mar 6 at 5pm Problem Set 3
Wed Mar 13 at 5pm Peer Review 3
Wed Mar 20 at 5pm Problem Set 4
Wed Mar 27 at 5pm Peer Review 4
Spring break
Wed Apr 17 at 5pm Problem Set 5
Wed Apr 24 at 5pm Project writeup + presentation slides due
Th Apr 25 8:40–9:55am Project presentations in discussion
Wed May 1 at 5pm Peer Review 5
Th May 2 8:40–9:55am Project presentations in discussion

Meetings

Readings are to be read after class, unless otherwise specified. Readings from R for Data Science (R4DS, Wickham et al. 2023) are intended as references that do not need to be read line-by-line but are a useful reference to support ideas.

Date Type Topic Reading
Jan 23 Lecture Welcome Jencks 2002 p. 49–53
Jan 25 Discussion Visualization R4DS Ch 1
Jan 30 Lecture Sampling: Simple random and unequal probability Probability sampling
Feb 1 Discussion Data transformation (day 1 / 2) R4DS Ch 3
Feb 6 Lecture Sampling: Stratified, clustered, and the future Groves 2011
Feb 8 Discussion Data transformation (day 2 / 2) R4DS Ch 3
Feb 13 Lecture Statistical learning Berk 2020 Ch 1 p. 1–5, stopping at paragraph ending “…is nonlinear.” Then p. 14–17 “Model misspecification…” through “…will always be in play.”
Feb 15 Discussion Statistical learning activity
Feb 20 Lecture Predicting life outcomes Optional: Salganik et al. 2020
Feb 22 Discussion PSID Income Prediction Challenge (day 1 / 2)
February break
Feb 29 Discussion PSID Income Prediction Challenge (day 2 / 2)
Mar 5 Lecture PSID Income Prediction: Awards and takeaways Optional: Lundberg et al. 2024
Mar 7 Discussion Racial wealth gap
Mar 12 Lecture Political origins of racial inequality
Mar 14 Discussion Class inequality exercise
Mar 19 Lecture Class + gender inequality
Mar 21 Discussion Project work
Mar 26 Lecture Asking research questions
Mar 28 Discussion Project work
Spring break
Apr 9 Lecture Moral arguments: Rawls Recap of ideas is here
Apr 11 Discussion Project work
Apr 16 Lecture Moral arguments: Nozick Recap in 3:49–5:05 here
Apr 18 Discussion Project work
Apr 23 Lecture Reducing inequality: Educational expansion Brand 2023 Ch 1
Apr 25 Discussion Project presentations
Apr 30 Lecture Reducing inequality: Reparations Before class, watch Ta-Nehisi Coates
May 2 Discussion Project presentations
May 7 Course summary
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