Data-Informed Economic Analysis

We are surrounded daily by claims built on data: "raising the minimum wage destroys jobs," "immigration lowers local wages," "this tax cut paid for itself." But which of these claims deserve our trust – and how would we verify them?

This course teaches students to think with data, not merely compute with it. Students will learn where economic data actually comes from, how to recognize when it is incomplete, misleading, or simply wrong, and how to transform a raw, disorganized dataset into a sound foundation for analysis. Along the way, they will develop the habits of a careful empirical thinker: exploring patterns, creating honest and informative visualizations, understanding uncertainty, and – crucially – recognizing why correlation is not causation.

The course requires no advanced mathematics and no prior programming experience – only curiosity about how the economy really works and a willingness to engage with real data. Whether the next step is econometrics, policy analysis, journalism, or business, this course equips students with the judgment to ask the right questions of data before any modeling begins.

By the end of the course, students should be able to take an economic question, locate and prepare suitable data, explore it carefully, communicate findings clearly, and understand what further analysis would be needed before making stronger causal claims. The course is designed as a bridge between introductory statistics and later econometrics courses.

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