How should I compare Georgetown and Purdue for undergraduate data science?

The biggest practical tradeoff is technical depth and scale at Purdue versus Georgetown’s Washington, D.C. access and interdisciplinary emphasis. Purdue’s undergraduate data science pathway is closely connected to computer science, statistics, and mathematics, with a large STEM-focused campus, extensive computing coursework, and recruiting ties to technical employers. Georgetown offers data science in a smaller liberal-arts environment where it can be especially valuable to connect quantitative work with public policy, economics, health, government, or ethics.

For preparation for data-focused careers or quantitatively rigorous graduate study, compare the required sequence course by course. Look for multivariable calculus, linear algebra, probability, mathematical statistics, programming, data structures or algorithms, databases, machine learning, and a substantial project or research experience. Purdue is likely to make it easier to build a highly technical transcript through advanced statistics, computer science, mathematics, and engineering-adjacent electives. That is particularly relevant for roles such as data scientist, machine-learning engineer, data engineer, or analytics work in technical industries.

Georgetown’s location is a real career advantage, but it points toward a different set of opportunities: federal agencies, nonprofits, policy research groups, consulting, global health, and technology-policy organizations. A student interested in using data to answer public-facing questions could gain unusually relevant internship experience during the academic year, not only over summers. Georgetown can also be a strong choice for graduate study when the student deliberately adds rigorous math, statistics, and computing courses and pursues faculty research.

For your stated goal of the strongest broad technical preparation in undergraduate data science, Purdue has the clearer edge. Choose Georgetown instead if you are equally drawn to data science and the policy, government, social-impact, or international contexts around it, and would actively use D.C. internships to build that specialization.