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Program Information

Graphic that shows that Education data science is an overlap of CS, Machine Learning, Statistics, Software Development and more


Students take a minimum of 18 courses, or 51 units: 9 from the core curriculum, 3 in educational foundations, and a minimum of 6 in at least 3 areas of data science specialization. In the summer following the first year of study, students complete an internship. Students also complete a capstone project under the mentorship of a GSE faculty member.

Curriculum and coursework

Core curriculum courses cover foundational data science concepts, developments in the field, basic programming skills, and statistical analysis. Students deepen their expertise in education with courses in a particular education domain, and they complete three of five data science specialization tracks.

Required courses

Note: All course information is subject to change. Please consult ExploreCourses and Axess for final course offerings.

Basic statistics

Students will be required to take two courses in statistics in order to employ these analyses in their data science courses later in their course of study.


Students are recommended to take these courses over the first two quarters to complete this requirement; however, any introductory statistics sequence up through multivariate regression or demonstrated equivalency will suffice and students can take more advanced statistics courses with consultation and approval from the MS Program Director.

Education foundation

Students must develop domain expertise to be effective education data scientists. Students will complete 3 education courses that ensure each student possesses deep knowledge of a specific domain of education theory and practice. Students may select courses that focus on an area such as Education Policy and Analysis, Learning Sciences, or Assessment. Students may design with consultation and approval from the MS Program Director a coherent set of education courses that advances their intellectual goals.

Data science specializations

Students will fulfill this requirement by completing three of five available tracks, each composed of two courses (see table below). The areas of concentration that will be offered are Natural Language Processing, Network Science, Experiments & Causal Methods, Measurement, and Learning Analytics (under development). These courses are established courses at Stanford University and will allow for interprofessional education of GSE students and graduate students from other departments.


The rigorous course schedule for the Education Data Science program offers relatively little opportunity for selecting elective courses during the first year of the program; however, second year students are encouraged to select an elective course in each of their final two quarters. Students are encouraged to take courses within the GSE relevant to their capstone projects, specializations, or research interests.

Student Voices

Hear from our students about why they chose to study education data science at Stanford, what their learning journey has been like, and what advice they would give to future EDS students.

What you need to know

Admission requirements

To learn more about requirements for admission, please visit the Application Requirements page.

Financing your education

To learn more about the cost of the program and options for financial support, please visit Financing Your Master’s Degree on the admissions website.

Contact admissions

For admissions webinars and to connect with the admission office, see our Connect and Visit page.

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