What does it mean to "read" data? This course introduces students to data as a social and analytical artifact, shaped by the questions we ask, the methods we use, and the communities we represent or overlook. Drawing on accessible readings, students build foundational skills in data literacy, data ethics, and exploratory analysis using spreadsheet and basic programming tools. 

In interactive Zoom sessions, the course moves through the full arc of working with data — from asking a well-formed question, to finding and cleaning a dataset, to interpreting what the numbers do and don't say. Sessions pair hands-on spreadsheet work with critical discussion, so that technical skills and analytical skepticism develop together. Students will regularly examine real datasets drawn from public health, policy, economics, and civic life, practicing both the mechanics of analysis and the habit of asking whose stories the data tells and whose it misses. In addition to our weekly meetings, students will be expected to spend an hour each week doing substantive work on discussion boards based on work done in our class sessions. This is in addition to regular homework assignments.

In asynchronous readings, case study responses, and a culminating data question project, students will build the reflective practice of a careful data reader — one who can spot a misleading visualization, evaluate a statistical claim, and communicate findings honestly to a general audience. By the end of the course, students will be equipped to engage with data not just as a technical object, but as a social one: shaped by choices, embedded in power, and always in need of interpretation.

This is not a statistics course. It is an inquiry into how data is made, who makes it, what it can and cannot tell us, and how to work with it honestly. Students will learn to ask better questions, recognize misleading claims, and develop practical skills in data organization and analysis — all within a framework that takes power, positionality, and context seriously.

No prior math or technical background is required.

Anticipated Credit Equivalencies: 

2 - Data Ethics

2 - Data Analytics