What are Barnard data science classes like for undergraduates?

Barnard undergrads can study data science through both Barnard and Columbia, and the classes are generally a mix of technical foundations and applied, hands-on work. You should expect coursework in programming, statistics, machine learning, data analysis, and data ethics, with many classes involving coding assignments and real datasets rather than only abstract theory.

At the introductory level, students usually build core skills in Python, probability, statistical inference, and data wrangling. As you move up, courses can include machine learning, data visualization, algorithms, databases, and domain-based applications in fields like economics, biology, public health, or the social sciences. That means the coursework is not just “learn the math,” but also “use the tools to answer actual questions.”

In practice, the balance is usually neither purely theoretical nor purely vocational. The stats, math, and computer science pieces give you theory, especially if you take courses through Columbia’s departments, but most undergraduate-facing data science classes include projects, labs, or problem sets where you clean data, write code, interpret output, and explain results.

One useful thing about Barnard is that data science is often interdisciplinary rather than siloed. A student might pair technical courses with work in political science, neuroscience, environmental science, or economics, and that shapes what the classes feel like. So if you want a program that is only about software engineering, it may feel broader than that, but if you like combining coding, statistics, and real-world questions, Barnard’s setup is a strong fit.