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Education Data Science (EDS)

Program Requirements

Students will take a minimum of 18 courses (51 units over 18 months) in order to complete their Education Data Science program. There are several requirements:

  • Minimum of 10 courses from the core curriculum including education data science courses, statistics courses, EDS seminar, and the EDS internship course.
  • Minimum of 3 courses for the Educational Foundation.
  • Minimum of 6 courses in at least 3 areas of data science specialization.
  • A minimum of 17 units must be completed for a letter grade.
  • A 3.0 GPA must be maintained for all courses applied to the master's degree.
  • Students must enroll in a minimum of 8 units during Autumn, Winter, and Spring Quarters, 1 unit in Summer, and cannot exceed 18 units in any quarter.
  • Note: if you wish to maintain eligibility to receive financial aid (such as loans), you must enroll in at least 8 units during the academic year and at least 6 units during summer.
  • All courses must be at or above the 100 level – courses numbered below 100 do not count toward the MS degree.
  • At least 25 units must be at or above the 200 level (EDUC 180 or 190 count toward this requirement).
  • At least 30 units must be from courses offered by the Graduate School of Education (EDUC units).
  • English for Foreign Students (EFSLANG 600 level) and Athletics, Physical Education and Recreation (ATHLETIC) courses cannot be applied towards the master's degree.
  • EDS students will design a course of study in consultation with the Program Director to ensure individual training goals are met.

The goal of the EDS MS is to train the next generation of data scientists who have a substantive background and concern with educational topics. The requirements are aimed at accomplishing this goal, but we recognize students may come in with more developed skills and background in certain areas and greater deficits in others. As a result, it may be advisable for students to request changes to course requirements, substituting various courses and building expertise where needed so as to make sure the EDS program trains students to be the best education data scientists possible. To this end, students can propose substituting certain course requirements after discussion, review, and approval by the program director so as to make sure training goals are satisfied.

Core Sequence

Note: All course information is subject to change. Please consult ExploreCourses and Axess for final course offerings. Not all courses are visible in Stanford ExploreCourses yet as they are uniquely developed for the EDS MS and have not been offered before.*
* The Education Data Science Internship (Summer) course will be posted here at a later date.

Introduction to Education Data Science

EDUC 423A "Introduction to Data Science: Data Processing" and EDUC 423B "Introduction to Data Science: Data Analysis" is a sequence of two courses that focus on working with education data using R (tidyverse and tidymodels package style). The first course focuses on how you can thoughtfully assess, manage, clean and represent data. The second course moves to an overview of various data science techniques to understand social phenomena (supervised and unsupervised learning).

Education Data Science Seminar

Each quarter during the first year, students will enroll in a 1-3 unit seminar course (EDUC 259A-C) designed to introduce emerging topics in the field of education data science, review and discuss relevant developments and topics. The seminar includes guest speakers, professional development, student-led programming and learning, and working towards an EDS Seminar Paper (first year). 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. 

Education Data Science Internship

This summer course is designed around the EDS internship experience. Starting with a suitable internship agreement, students will explore personal learning goals, share experiences, reflect on their progress and development, and connect their internship to their past and future academic coursework. 

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.

Introductory

Advanced

Education foundation

Students must develop domain expertise in education to be effective education data scientists. To this end, students will complete 3 education courses that ensure each student possesses deep knowledge on education theory and practice. For example, students may select courses that focus on areas like Education Policy and Analysis, Learning Sciences, or Assessment (among others). Students may design with consultation and approval from the program director a set of education courses that advances their intellectual goals.

Data Science Specialization

Students must develop substantive breadth and depth in data science skills. To this end, students will complete three of four available tracks, each composed of two courses (see below). The areas of concentration that will be offered are Natural Language Processing, Network Science, Experiments & Causal Methods, Measurement, and Learning Analytics. These courses are established courses at Stanford University and will allow for interprofessional education of GSE students and graduate students from other departments.

Electives

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.

English for foreign students

Non-fluent speakers of English are strongly encouraged to take one of the following writing courses:

Sample program timeline
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