Social Data Science
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  1. Social Data Science
  • Summarizing Distributions
    • Empirical Questions
    • Using Data
    • Distributions and Summaries
    • Population sampling
    • Confidence Intervals
  • Models for Subgroup Summaries
    • Subgroup Summaries by Prediction
    • Forests
    • Data-driven selection of an estimator
    • Are complex models better?
  • Causal Inference under Measured Confounding
    • Defining causal effects
    • Exchangeability
    • Directed Acyclic Graphs
    • Models for causal inference
    • Continuous treatments
  • Causal Inference with Unmeasured Confounding
    • Difference in difference
    • Regression discontinuity
    • Instrumental variables
  • Software and Coding
    • Software Prerequisites
    • Visualization
    • Data transformation

Social Data Science

UCLA SOCIOL 114 (Winter ’26)

Together, we will use tools from data science to answer social science questions. As an area of application, we will focus on questions about inequality and social stratification.

Learning goals

As a result of participating in this course, students will be able to

  • connect theories about inequality to quantitative empirical evidence
  • evaluate the effects of hypothetical interventions to reduce inequality
  • conduct data analysis using the R programming language

Team

Ian Lundberg
ianlundberg@ucla.edu
(he / him)

Working with data to understand inequality brings me joy and meaning, as I first discovered as a college student years ago. I hope to share that joy with you! Other joys of mine include hiking, surfing, and oatmeal with blueberries.

Taylor Aquino
taquino7@g.ucla.edu

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