INFO 5371 - Studying Social Inequality Using Data Science

Spring. 3 credits. Student option grading.

Prerequisite: INFO 2950 or equivalent. Co-meets with INFO 3370.

I. Lundberg.

Inequality is high in American society. Income and wealth are concentrated in far fewer hands than in other industrialized countries. Labor market outcomes are patterned by disparities across lines of race, gender, and class. This course will introduce social science theories about the origins of inequality, emphasizing how inequality is transmitted over time and across generations. Building on these theories, students will deploy tools for data science to visualize inequality, understand inequality, and evaluate hypothetical policy interventions that might reduce inequality. We will use the R programming language. A theme of the course is that applied work in this area can give rise to new data science tools, which may help solve some of society’s most pressing challenges.

Outcome 1: Visualize economic inequality with graphs that summarize survey data.

Outcome 2: Connect theories about inequality to quantitative empirical evidence.

Outcome 3: Evaluate the effects of hypothetical interventions to reduce inequality.

Outcome 4: Conduct data analysis using the R programming language.