What is the typical academic path for a University of Chicago data science major?

At UChicago, the data science major is usually built on a strong math, statistics, and computer science foundation first, then moves into core data science methods and electives. In practice, students often start with calculus, introductory statistics, and programming, because the major is housed in a very quantitative environment and expects comfort with mathematical reasoning and computation. UChicago’s program is designed less like a narrow coding track and more like an interdisciplinary major that combines statistical thinking, modeling, and real data analysis.

A typical path begins in the first year or early second year with prerequisite work such as calculus, intro programming, and statistics. Students then move into the major’s core courses, which generally cover topics like data engineering, machine learning, statistical methods, and practical data analysis. The structure is meant to give both theoretical grounding and applied experience, so it is not just about learning tools like Python, but also understanding why methods work and when to use them.

From there, students usually take upper-level electives that match their interests, such as more advanced statistics, computer science, economics, social science applications, or domain-based data work. UChicago tends to encourage connecting data science to another field, so many students pair the major with areas like economics, public policy, biology, or social research. That makes the academic path fairly flexible after the core is complete.

In practical terms, a student interested in this major should expect a quantitatively rigorous course load, especially early on. Strong preparation in high school math helps, and some exposure to coding can make the transition easier, though it is not usually required to start. The overall path is best thought of as: foundational math and programming, core data science methodology, then specialized electives and applications.