Educational Data Science, PhD
Crafting practices and policies that solve our most challenging problems starts with research. Learn to use empirical data to improve everything from the classroom experience to systemic inequities.
Better Data, Better Practice
Every aspect of the educational experience is constantly evolving. The Educational Data Science doctoral program provides the foundation you need to study educational problems, influence policy or shape the future as a faculty member—in higher education, K–12 school settings or non-profit, governmental or educational research centers. Relying on educational big data, data mining or learning analytics will teach you to leverage big data and use critical quantitative methods harnessed for educational equity.
Championing Math Equity
Alumna Melodie Baker is dedicated to advancing educational equity in mathematics. Her early experiences with discriminatory tracking in elementary school, which limited her potential in math, inspired her to pursue a career in math education reform. After earning degrees in public relations and executive leadership, she joined United Way of Buffalo and Erie County as director of education. To deepen her impact, Baker enrolled in UB’s applied statistical analysis certificate program, where she rediscovered her passion for math and continued into the educational data science PhD program (formerly the educational psychology and quantitative methods PhD program). Now, as national policy director at Just Equations, she works to redefine the role of math in promoting equity, removing barriers, and improving access to math education for underserved populations.
Points of Distinction
Gain practical experience with big data and advanced analytics. Through hands-on projects, you'll learn to extract, analyze, and model educational data, transforming raw information into insights that enhance learning environments and educational outcomes.
Explore cutting-edge methodologies that emphasize equity and social impact. This program integrates critical approaches to data science, empowering you to apply data analytics in ways that support meaningful, positive changes in education and beyond.
As a STEM-approved program, international students have the opportunity to apply for a 24-month OPT extension, enhancing their career prospects and enabling extended, practical experience in educational data science.
Program Overview
| Academic credential granted | Doctor of Philosophy (PhD) |
|---|---|
| Credits required for completion | 72 |
| Time to completion | 3.5 to 5 years |
| Course delivery | On campus or hybrid (on campus with some online courses) |
| Application deadline | Fall: Jan. 1 |
Required Courses (20 credits)
- CEP 500 Fundamentals of Educational Research
- CEP 510 Psychometric Theory in Education
- CEP 522 Statistical Methods I (4 credits)
- CEP 523 Statistical Methods II (4 credits)
- CEP 541 Human Growth and Development
- CEP 560 Psychology of Learning and Instruction
Distribution Requirements (27 credits)
Data Analytics (choose two)
These courses are currently under development. Completing CEP 512 or CEP 533 will fulfill this requirement until official replacements become available in fall 2025.
- CEP ### Foundations of Educational Data Science and Analytics (Currently, CEP 512)
- CEP ### Advanced Programming for Educational Data Analytics (Currently, CEP 533)
Measurement and Statistics (choose three)
- CEP 524 Experimental Design
- CEP 525 Applied Multivariate Analysis
- CEP 526 Structural Equation Modeling
- CEP 527 Analysis of Large-Scale Databases
- CEP 528 Hierarchical Linear Modeling
- CEP 529 Applied Regression Analysis
- CEP 530 Introduction to Meta-Analysis
- CEP 531 Applied Survival Analysis in Education and Social Sciences
Substantive Issues of Educational Policy and Practice (choose two)
- CEP 533 Topical Seminar
- ELP 566 Comparative and Global Studies in Education
- ELP 575 Education and Globalization
- ELP 585 Sociological Bases of Education
- ELP 592 American Education for International Students
- ELP 615 Critical Race Theory
- ELP 620 Educational Transitions P–20
- ELP 626 Policy Formulation and Analysis
- LAI 512 Readings in Multicultural Literature
- LAI 525 Science, Technology and Human Values
- LAI 529 Computers in Early and Primary Education
Qualitative Research Methods (choose two)
- ELP 593/594 Qualitative Research Methods Part I and Part II (2 semesters)
- LAI 515 Action Research to Improve Teaching and Learning
- LAI 625 Discourse Analysis Research
- LAI 669 Qualitative Techniques in Educational Research
- LAI 619 Qualitative Research Design (prerequisite: LAI 669)
- LAI 626 Advanced Qualitative Research in Field Methods (prerequisites: LAI 669 and 619; permission of advisor and IRB approval)
- NUS 697 Advanced Qualitative Methods
Qualifying Paper and Dissertation (15 credits)
- CEP 700 Individual Guidance Project (PhD Qualifying Paper; 3 credits)
- CEP 702 Individual Guidance Dissertation (12 credits)
Example Schedule
Fall 1
- CEP 500 Fundamentals of Educational Research
- CEP 522 Statistical Methods I
- CEP 541 Human Growth and Development
- Choose one data analytics course
Spring 1
- CEP 510 Psychometric Theory in Education
- CEP 523 Statistical Methods II
- CEP 560 Psychology of Learning and Instruction
- Choose one substantive issues course
Fall 2
- Choose two measurement and statistics courses (CEP 524–531)
- Choose one qualitative research methods course (e.g., ELP 593)
- Choose one substantive issues course (e.g., CEP 533)
Spring 2
- Choose one measurement and statistics course (CEP 524–531)
- Choose one qualitative research methods course (e.g., ELP 593 or LAI 669)
- Choose one data analytics course
- CEP 700 Qualifying Paper
Fall 3
- Elective
- CEP 702 Dissertation
Spring 3
- Elective
- CEP 702 Dissertation
Fall 4
- Elective
- CEP 702 Dissertation
Recommended Electives
General
- CEP 594 Practicum in Teaching
- CEP 703 Independent Study
- ELP 589 Education and Socialization
Research Design, Measurement and Assessment, Statistics and Data Analysis
- CEP 502 Assessment of Learning and Instruction
- COM 636 Research Procedures in Communication
- LAI 638 Using Rasch Modeling to Develop Measurement Instruments
- NUS 695 Advanced Statistical Techniques
- NUS 706 Psycho-Social Measurement and Questionnaire Construction
- SOC 608 Social Statistics II: Causal Models (Advanced regression techniques)
Research and Assessment Courses in the Disciplines
- LAI 547 Assessing Mathematics
- LAI 569 Introduction to Research in Music Education
- LAI 623 Research Seminar in English Education
- LAI 637 Research in Science Education
- LAI 640 Seminar in Mathematics Education Research
- LAI 657/658 Analysis of Quantitative Research in Learning and Instruction
- LAI 686 Critical Interpretations of Research (multiple disciplines)
Standardized Testing No Longer Required
In an effort to make the application process more equitable, the GRE/MAT is no longer required for admissions consideration. If you still plan to take the GRE/MAT exam, you can send your scores through the testing agency, and they will automatically be added to your application; however, they are not required for admission consideration to this program.
You must have a baccalaureate degree from an accredited institution.
Submit your completed online application, which includes:
- Application fee: A $50 non-refundable application fee, submitted electronically through UB's ePayment system.
- Contact information for at least three individuals who will each be asked to provide an electronic recommendation letter.
- Unofficial transcripts from all colleges attended. (UB transcripts are automatically submitted for current UB students and alumni.)
- Statement of interest: Statement of your career goals and objectives.
Former/Maiden Name: Please provide us with your former/maiden name if you have one. When requesting transcripts, please ask the sending institution to indicate your current name and former/maiden name.
Admission Decision: The admission decision will be communicated to you as soon as review is complete. The decision is based on a number of factors and is the result of a thorough and deliberate process. All decisions are final and cannot be appealed.
In-State Residency Tuition
In order to qualify for the in-state residency tuition rate, you must provide residency documentation indicating you have lived in New York State (NYS) 12 months before your semester start date.
If accepted, you will need to upload three documents to qualify for the in-state tuition rate. See Required Documents for Residency Application for more information.
International Applicant Additional Requirements
- Official original proof of your degree
- A copy of your passport biographical page
- Official test scores—dated within two years of submitting your application—for either the TOEFL (Test of English as a Foreign Language), the IELTS (International English Language Testing System) Academic Test, the PTE (Pearson Test of English) Academic, or DET (Duolingo English Test).
- TOEFL minimum score is 250 for computer-based test, 600 for paper-based test and 79 for internet-based test
- IELTS Academic Test minimum score is 6.5 overall
- PTE minimum score is 55 overall
- DET minimum score is 120 overall
- Financial documentation: International graduate applicants must document their ability to pay for all costs incurred while studying in the U.S.
- An official bank statement
All financial forms and supporting documentation with required signatures must be uploaded with your application and dated within one year of your intended enrollment date.
Graduates with a PhD in educational data science can pursue employment in the following areas:
- In academia and research, PhD graduates from an Educational Data Science program can work as faculty members specializing in educational data science, evaluation, and assessment. These roles focus on conducting research to drive educational innovation, improve learning outcomes, and apply technology in education. Faculty members analyze data to assess educational programs, evaluate student performance, and measure instructional effectiveness, contributing to data-informed decisions that shape policy and practice in the field.
- In K–12 and higher education institutions, they can work as data analysts or learning analytics specialists. These roles focus on using data to enhance student outcomes, optimize curriculum design, and support informed institutional decision-making.
- In the government and public policy sector, data science graduates can influence educational policy at local, state, or national levels. They can hold positions in policy development, program evaluation, or research analysis, using data to assess the effectiveness of educational programs and inform policy decisions.
- Nonprofit organizations and NGOs often seek data science graduates to support mission-driven initiatives aimed at improving educational equity and outcomes for underserved communities. These roles may involve educational research, program evaluation, and data management.
- Corporate training and development departments offer data science graduates opportunities to use data in optimizing employee training programs, professional development initiatives, and performance assessments. Graduates help organizations make data-driven decisions to enhance workforce development.
Associate Professor
Educational Leadership And Policy
Research Areas: Access and Equity; Achievement; Big Data; Educational Policy; Gender, Culture, and Equity; Global Issues; Higher Education; International Education; Quantitative Research Methods; Race, Inequality, and Education; Sociology of Education; STEM education
Associate Professor
Counseling, School And Educational Psychology
Associate Professor
Learning And Instruction
Research Areas: Access and Equity; Achievement; Assessment; Big Data; Curriculum and Instruction; Design and Analysis of Longitudinal Research; Design Experiments; Digital Innovations; Digital Media and Learning; Diversity; Educational Policy; Evaluation; Gamification; Immigrant Issues; Large-Scale Assessment and Research; Mathematics Education; Online and Distance Learning; Professional / Staff Development; Psychometrics; Quantitative Research Methods; Race, Inequality, and Education; Research Design; Research Methods; School and District Reform; Statistical Methods and Applications in Statistical Issues
Professor
Counseling, School And Educational Psychology
Professor
Educational Leadership And Policy
Research Areas: Access and Equity; Big Data; Educational Policy; Higher Education; International Education; Large-Scale Assessment and Research; Leadership; Program Evaluation; Psychometrics; Quantitative Research Methods; Race, Inequality, and Education; Social and Emotional Development; Social Justice; STEM education
Clinical Associate Professor
Counseling, School And Educational Psychology
Research Areas: Achievement; Cognition; Cognitive Psychology; Online and Distance Learning; Quantitative Research Methods
Assistantships are available in a variety of areas that change from year to year. Compensation for positions ranges from hourly pay only to a stipend to tuition remission plus a stipend. Generally, assistantships require 20 hours of work per week.
The application process for assistantships is separate from the program admission process. Assistantship opportunities are posted for the following academic year in mid-January, from offices on campus such as Campus Living, Career Services, Student Engagement, Intercultural and Diversity Center, among others.
While assistantships are limited, new opportunities become available throughout summer and into the fall semester.
Faculty Expertise
Associate Professor Sunha Kim has questions. And they cover a broad spectrum. How can libraries improve retention of librarians who are Black, Indigenous or people of color? What comes of the digital divide after expanding high-speed Internet access? How can Western New York school districts attract and retain diverse and effective science teachers? These are among the projects for which Kim has received over $1.2 million in funding. They don’t include four five-year NSF grants on which she is an evaluator. In all, they reflect her commitment to interdisciplinary collaboration and research.