Major: Bachelor of Science

Mission of the Undergraduate Program in Data Science  

The undergraduate program in Data Science aims to provide students with an analytical and quantitative foundation for tackling data-driven problems in science, industry, and society. Data science is an interdisciplinary field combining computational and inferential reasoning to extract knowledge or insights from data for use in various applications. It synthesizes the most relevant parts of foundational disciplines to solve particular problems or applications. As more data and new ways of analyzing data become available, our economy, society, and daily life will become even more dependent on our ability to learn from data systematically. 

Students pursuing a BS in Data Science will acquire a core foundation of mathematics basic to all the mathematical sciences and be introduced to concepts and techniques of computation, optimal decision-making, probabilistic modeling, and statistical inference. Beyond this foundation, students can explore how inferential and computational thinking can be effective in areas as diverse as finance, biology, marketing, and engineering; or they can choose to acquire greater depth in one of our core disciplines. The BS in Data Science is an ideal major to prepare students for graduate study in quantitative fields, such as computer science and statistics, and for careers in various industries that require quantitative work, such as information technology and finance.

The Data Science program is interdisciplinary in its focus and sponsored by Stanford’s departments of Statistics, Mathematics, Computer Science, and Management Science & Engineering. Students are required to take courses in each of these departments. Students are required to choose a subplan: The Mathematics and Computation subplan allows students to explore the core subjects further. In contrast, there are three other subplans available for students who are interested in data science's applications in one of the following areas: biology and medicine, computational neuroscience, or quantitative finance.