Education Data Science Master's Program Information
Overview
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.
Each quarter, you will enroll in a 1-unit seminar course designed to introduce emerging topics in the field of education data science, review and discuss relevant developments and topics. The seminar will include a variety of topics and activities. First, the EDS seminar is designed to develop a strong sense of community among students by providing space where students can learn about and from each other's varying perspectives and experiences across course work. Second, the seminar will engage leaders in the field. During such sessions, you will have an opportunity to meet guest speakers and trailblazers (some of whom may be providers for summer internships and/or prospective future employers) so you learn about contemporary challenges and innovation in the field. Third, the seminar will also bring together students and faculty in the education data science program to create a scholarly community to learn and support each other’s work. Lastly, the seminar will provide space for professional development, such as critical thinking, developing and processing constructive feedback, time-management, and communication skills.
This summer course is designed around the EDS internship experience. Starting with a suitable internship agreement, we will explore your personal learning goals, share experiences, reflect on your progress and development, and connect your internship to your past and future academic coursework.
In the second year of the program, seminar sessions will focus on student capstone projects, providing opportunity for collaboration and feedback, and time for final presentations of projects in the final quarter.
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.
Offered in Spring
2021-2022
(MacCartney, B.; Potts, C.; Bhaskaran, J.; Farhangi, A.; Geiger, A.; Kim, M.; Li, L.; Wang, C.; Yerukola, A.; Zhou, X.)
(3-4) Offered in Spring
2021-2022
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.
