STS 150
Data for Justice Fall 2024
Division II Quantitative/Formal Reasoning Difference, Power, and Equity
Cross-listed AMST 150 / SOC 150 / WGSS 150 / INTR 150

Class Details

This course is a unique and inclusive introduction to data science where quantitative thinking, programming, and social justice intertwine. We will build our data science skills using R, a popular open-source data science tool. We will focus on essential stages of data analysis, including data acquisition, cleaning, wrangling, visualization, and exploration. But rather than divorcing these techniques from the social issues they can help illuminate, we ground them in a social justice context. Overall, we will apply data science skills to topics drawn from criminal justice, environmental justice, diversity and inclusion in arts and media, education equity, and much more, with the goal of growing our collective capacity to use data science as a tool for social good. During a time when humans are increasingly subjugated to data-driven algorithmic decisions, when there are social media accounts dedicated to highlighting misuses of data, and when artificial intelligence makes faking data a nearly trivial task, using data to ethically and carefully promote justice is more important than ever.
The Class: Format: lecture; This course is taught in a highly interactive format and will frequently use a flipped-classroom approach. Students should expect substantial time devoted to in-class collaboration.
Limit: 18
Expected: 18
Class#: 1036
Grading: no pass/fail option, no fifth course option
Requirements/Evaluation: Students will complete regularly assigned activities, problem sets, and other assessments. To move towards a non-hierarchical, transparent, and egalitarian grading system, the instructor adopts a mastery-based approach.
Prerequisites: None. This course assumes no prior knowledge of data science or R programming. An interest in social justice and a willingness to engage intensively with data and computing are essential.
Enrollment Preferences: Students without prior college-level courses in statistics and programming.
Distributions: Division II Quantitative/Formal Reasoning Difference, Power, and Equity
Notes: This course is cross-listed and the prefixes carry the following divisional credit:
STS 150 Division II AMST 150 Division II SOC 150 Division II WGSS 150 Division II INTR 150 Division II
DPE Notes: This course uses data science as a lens for injustice in spheres such as criminal justice, environmental justice, diversity and inclusion in arts and media, education equity. We will consider race, gender, LGTBQ+, disability, and other axes of identity. Additionally, we will adopt a data-critical perspective, thinking about how social forces shape data and our understanding of it.
QFR Notes: This course teaches quantitative tools in R, a widely-adopted data science platform. We will focus on essential stages of data analysis, including data acquisition, cleaning, wrangling, visualization, and exploration.

Class Grid

Updated 2:13 pm

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