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Students & Alumni

The master of science in education data science draws students from around the world and diverse backgrounds in education, computer science, statistics, learning technology, and more. What they share is a passion for applying data science skills and techniques to education research and practice.

Our students

Othman Bensouda Koriachi

Othman Bensouda Koraichi

Class: 2023
Areas of interest: poverty and inequality, assessment, testing, measurement, literacy and language, data science, economics and education, education policy, international and comparative education
Ian Bott

Ian Bott

Class: 2023
Areas of interest: digital learning, education technology, entrepreneurship, emerging technologies, neuroscience, AI, AR/VR, learning analytics, adaptive learning, social networks
Lucy Caffrey-Maffei

Lucy Caffrey-Maffei

Class: 2023
Areas of interest: race and inequality, K-12 education, urban education, traditional public education, education policy, housing equity, sociology of education, equitable access to and resources in education, assessment and accountability of student growth and achievement
Shenghan Chen

Shenghan Chen

Class: 2023
Areas of interest: math education, personalized/adaptive learning, educational equity, EdTech products, learning analytics, AI, AR, blockchain in education, neurodiversity and mental health
Xunyi (Annie) Gao

Xunyi (Annie) Gao

Class: 2023
Areas of interest: data sciences, technology and education, cognition and learning
Laura Hinton

Laura Hinton

Class: 2023
Areas of interest: educational equity, diversity in STEM education, data visualization, antiracist data science
Akshatha Kamath

Akshatha Kamath

Class: 2023
Areas of interest: education technology, special education
Elena Pittarokoili

Elena Pittarokoili

Class: 2023
Areas of interest: educational technology, learning analytics, natural language processing, measurement in education, entrepreneurship in education, women in STEM, EdTech startups
Anna-Julia Storch

Anna-Julia Storch

Class: 2023
Areas of interest: educational technology, entrepreneurship, lifelong learning, assessment, AI, learning analytics, product management, growth mindset, mentoring, school reform, future of work (re- and upskilling), sports in education
Mei Tan

Mei Tan

Class: 2023
Areas of interest: learning analytics, technology in classrooms, educational technology, social computing, K–12 education, cognition of learning, learning experience design, inclusion and equity
Richard (Chenming) Tang

Richard (Chenming) Tang

Class: 2023
Areas of interest: data science, machine learning, edtech entrepreneurship, higher education, upward social mobility, education equity, lifelong learning, professional development, college-to-career success, human-centered design, digital transformation in education
Raymond Zhang

Raymond Zhang

Class: 2023
Areas of interest: educational AI, after school activities, informal education, identity development, critical consciousness development, diversity and inclusion, education policy, learning technology, STEM education
Sibei Zhang

Sibei Zhang

Class: 2023
Areas of interest: educational technology, learning analytics, data visualization, personalized learning, K–12 education, school reform, math education, professional development, growth mindset, design thinking, product development, entrepreneurship in education
Priscilla Zhao

Priscilla Zhao

Class: 2023
Areas of interest: educational research, assessment of learning and understanding, learning experience, gameful learning, early childhood education, literacy development, equity in education, artificial intelligence for learning
Iris Zhong

Iris Zhong

Class: 2023
Areas of interest: measurement and assessment, longitudinal data analysis, learning analytics, equity, education technology, language acquisition, early childhood education

Life after EDS

The job market for professionals with advanced data analysis and visualization skills is growing across all sectors. Data science professionals with expertise in education bring to the field their deep understanding of learning, schools, universities, and education policy.

Examples of potential employers include education research firms, school districts and systems, universities, state and federal government education agencies, ministries of education outside the U.S., education technology companies, and educational for-profit ventures.

Education Data Science students and alumni have access to ongoing career development services through the Graduate School of Education, as well as through Stanford Career Education.

Explore GSE career services

Explore Stanford Career Education

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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