Data-Informed Economic Analysis

This Bachelor-level course introduces students to the practical foundations of empirical work in economics. Rather than focusing on formal econometric models, the course teaches students how to work with real-world data before advanced analysis begins. Students will learn how to find relevant data, assess its quality, clean messy datasets, deal with missing or incomplete information, combine data from different sources, and organize their work in a transparent and reproducible way. A major part of the course will be devoted to exploratory analysis: summarizing data, identifying patterns, creating meaningful visualizations, and recognizing when graphs or summary statistics may be misleading. The course will also strengthen students’ understanding of core statistical ideas, including variation, sampling, uncertainty, confidence intervals, hypothesis testing, and simple relationships between variables. Alongside these tools, students will be introduced to the logic of causal thinking: why correlation is not the same as causation, how mechanisms matter, and how bias or confounding can shape what we observe in the data.

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.

‍