Social Data Science
  • Syllabus
  • Team
  • Schedule
  • Office Hours
  • Piazza
  • Forms
  1. Home
  • Home
  • Working with Data
    • Asking questions with data
    • Setting up your computer
    • Visualization
    • Data transformation
  • Inference Without Models
    • Population sampling
    • Using weights
    • Defining causal effects
    • Exchangeability
    • Conditional exchangeability
    • Nonparametric estimation
    • Directed Acyclic Graphs
  • Inference with Models
    • What is a model?
    • Why model?
    • Models for causal inference
    • Matching
    • Trees
    • Forests
    • Bootstrap Inference
    • Data-driven selection of an estimator
  • Discussion Meetings
  • Assignments
    • Problem Sets
      • Problem Set 0
      • Problem Set 1
      • Problem Set 2
      • Problem Set 3
      • Problem Set 4
    • Project

Social Data Science

UCLA SOCIOL 114 (Winter ’25)

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

  • visualize economic inequality with graphs that summarize survey data
  • 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
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Working with Data