Master of Science

The Biomedical Data Science Program is interdepartmental and offers instruction and research opportunities leading to MS and PhD degrees in Biomedical Data Science. All students must complete the core curriculum requirements and additional coursework that fulfills degree requirements and pursues their technical interests and goals as specified for each degree program.

The program can provide flexibility and complement Stanford’s other applied medical research opportunities. Special arrangements may be made for those with unique needs or those simultaneously enrolled in other degree programs within the university. Similarly, students with prior relevant training may have the curriculum adjusted to eliminate requirements met as part of prior training.

The GRE is not required for admission.

See Graduate Degrees for university requirements for the MS degree.

Advising

Upon entering the program, each student is assigned an academic advisor to help with course selection and monitor progress toward program milestones and degree requirements. Each research-track (academic) MS and PhD student conducts research under the primary mentorship of a faculty supervisor, who guides their scholarly and professional development. Further details about the advisor roles and responsibilities are found in DBDS Student Handbook opens in a new tab.

Master of Science in Biomedical Data Science (Academic)

This degree is designed for individuals who wish to undertake an in-depth study of biomedical data science with research on a full-time basis. Typically, a student spends two years in the program and implements and documents a substantial project by the end of the second year. The first year involves acquiring the fundamental concepts and tools through coursework and research project involvement. Academic MS students are expected to devote 50 percent or more of their time to participating in research projects. Research rotations are not required but can be done with the approval of the academic advisor or training program director. Graduates of this program are prepared to contribute creatively to basic or applied projects in biomedical data science. This degree requires a written research paper to be approved by two faculty members.

Master of Science in Biomedical Data Science (Professional/Honors Cooperative Program)

This degree is designed primarily for the working professional with advanced training in one discipline who wishes to acquire interdisciplinary skills. Although many classes necessary for the degree are available online, some requirements may be fulfilled by implementing an alternative plan to be approved by the program. The professional MS is offered with the Stanford Engineering Center for Global & Online Education (CGOE), which establishes tuition and fee rates. The program uses the honors cooperative program (HCP) model, which assumes that the student works full time and is enrolled in the MS part-time. Students who live locally may attend their courses on campus. Students have up to five years to complete the program. Research projects are optional; the student must make arrangements with program faculty if interested. Graduates of this program are prepared to contribute creatively to basic or applied projects in biomedical data science.

Master of Science in Biomedical Data Science (Coterminal)

The coterminal degree program allows Stanford University undergraduates to study for a master’s degree while completing their bachelor’s degree(s) in the same or a different department. See Coterminal Master's Degrees opens in a new tab for additional information. 

The coterminal Master of Science program follows the same program requirements as the Master of Science (Professional), except for the requirement to be employed full-time. The coterminal degree is available only to current Stanford undergraduates. Coterminal students are enrolled full time, and courses are taken on campus. Research projects are optional; the student must make arrangements with program faculty if interested. Graduates of this program are prepared to contribute creatively to basic or applied projects in biomedical data science.