DATSC-BS - Data Science (BS)

Mission of the Undergraduate Program in Data Science  

The mission of the undergraduate program in Data Science is 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 that combines computational and inferential reasoning to extract knowledge or insights from data for use in a broad range of applications. It synthesizes the most relevant parts of foundational disciplines in order to solve particular classes of 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 systematically learn from data. 

Students pursuing the B.S. 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 B.S. 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 a range of industries that require quantitative work, such as information technology and finance.

B.S. Degree Requirements

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. A computational biology track is available for students interested in applications of mathematics, statistics, and computer science to the biological sciences (bioinformatics, computational biology, statistical genetics, neurosciences); and in a similar spirit, there are engineering and statistics options.

External Credit Policies

Course transfer credit is subject to department evaluation and to the Office of the Registrar's external credit evaluation. These courses may result in a replacement course for Data Science required course or may establish placement in a higher-level course. Transfer requests must first be submitted to Student Services Center prior to being evaluated by your advisor.

Transfer Credit for Data Science Declared Students

Transfer credit must be approved first by the University Registrar's Office (and listed on your Stanford transcript), and then by the department before it can be used towards your Data Science major requirements. It is the student's responsibility to compile the documents required for course transfer evaluation form.

1. Complete a Data Science Course Equivalency Petition for each course and attach all necessary documentation (course description, syllabus, and unofficial transcript).

2. Submit the above documents to the student services officer in Sequoia Hall, Room 124.

3. Ask the Student Services Center to send a copy of the specific course approval paperwork (Request for Transfer Credit Evaluation form) to the department program administrator: Sequoia Hall, room 124 - Data Science program.

Unit Information

To be recommended for the B.S. degree in Data Science, the student must complete at least 79 units in the program.

Students in the Data Science Program are expected to achieve the following learning outcomes. These learning outcomes are used both in evaluating students and the undergraduate program. By the time they graduate, majors are expected to:

1. Frame questions of interest from a variety of disciplines in quantitative terms and identify what data types might be useful in addressing them.
   

2. Demonstrate familiarity with the key statistical, mathematical, and computational concepts and use them appropriately.
   

3. Appraise the major ethical questions that arise in the collection, analysis, and use of data for decision-making based on quantitative reasoning.
   

4. Convey quantitative analysis and technical results to a wide audience, effectively communicating the uncertainty associated with their conclusions, and taking care to assure the reproducibility of results.

Additionally, students in the Bachelor of Science in Data Science major are expected to:

5. Critically evaluate modeling, inferential and computational approaches for solving the problem at hand.  Specifically, they will be able to:  

(5a.) Justify different approaches on the basis of mathematical and statistical reasoning.

(5b.) Implement the chosen strategy with computationally efficient algorithms and following modern software engineering principles.

(5c.) Test it empirically.

(5d.) Assess its limits.

6. Contribute creatively to the theoretical and/or applied frontiers of at least one of the following disciplines: statistical analysis, optimization, computation, and mathematical modeling.