Boston University vs University of Maryland for data science: which is better for undergrad?

The biggest practical tradeoff is cost and access to a large public-school tech ecosystem at Maryland versus BU’s urban Boston setting and private-school feel. For data science specifically, Maryland tends to stand out more on pure computing depth because of its very strong computer science reputation, broad technical course options, and proximity to major employers around D.C., College Park, and nearby research hubs. BU offers solid quantitative training too, but Maryland usually gives undergrads a deeper bench of computing-related opportunities at scale.

For coursework, Maryland has an edge if you want flexibility across computer science, math, statistics, and applied technical electives. Its strengths in CS, machine learning, and related research can make it easier to build a more technical data science foundation, especially if you may later lean toward software, AI, or graduate study. BU can be attractive if you want a more contained campus experience inside a major city and easy access to Boston’s biotech, healthcare, and finance sectors, where data roles are plentiful.

For internships, both are well placed, but in different ways. Maryland benefits from connections to government, defense, consulting, research labs, and large employers in the D.C. area, which can be especially useful for analytics, public-sector tech, and research-oriented work. BU benefits from Boston’s startup, healthcare, and finance landscape.

For grad school preparation, Maryland again has a slight advantage because of its research environment and stronger overall identity in computing. If the costs are close, I would lean Maryland for the stronger overall undergraduate platform in data science. BU is still a good option, but it makes the most sense if you strongly prefer Boston or BU’s campus style.