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 or research assistantship. Students also complete a capstone project under the mentorship of a GSE faculty member.
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.
Students must develop domain expertise to be effective education data scientists. To ensure a deep knowledge of a specific domain of education theory and practice, students complete three courses in an area such as education policy, learning sciences, or assessment. Students may also design a set of education courses that advances their intellectual goals.
Students will complete data science specializations by completing three of five available tracks, each composed of two courses. Areas of concentration include: natural language processing, network science, experiments and causal methods, measurement, and learning analytics. Specialization courses are established courses at Stanford University and will allow for interaction with graduate students from other departments.
The rigorous course schedule for the program offers relatively little opportunity for electives during the first year, though students can 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.
Students’ preparation as data science professionals is best developed by applying analyses to real-world situations and solving educational problems in real time. During the summer, students take an internship or research assistantship to facilitate their data science training and integrate knowledge acquired in their courses and seminar sessions. These practical experiences are designed to reinforce course learning while developing research and critical thinking skills and acquiring new knowledge in students’ area of specialization.
In the second year, students complete a capstone project under the mentorship of a faculty advisor. The project requires students to take what they have learned throughout the program and apply knowledge and theory to a real-world data project. In collaboration with faculty, students draw on their chosen specialization and their internship/assistantship experiences to design their projects, and begin data collection and/or analysis during the summer term. Winter term of the second year is fully dedicated to research, writing, and preparation for presentation of the capstone project to faculty and outside experts.