Educational Data Science, Graduate Certificate
The Educational Data Science Graduate Certificate is designed for graduate students interested in new-often digital-data sources and analytic methods in educational contexts.
Program overview
The Educational Data Science Graduate Certificate, offered through Theory and Practice in Teacher Education, prepares graduate students to work with emerging digital data sources and analytic methods in educational contexts—such as learning management systems and social media-based professional networks. Coursework covers data wrangling, visualization, functional programming, structured and unstructured data, machine learning applications, and ethics, privacy, and justice in data science.
The certificate requires 12 graduate credit hours across four courses plus a capstone (CADE 680, CADE 685, CADE 691, and STEM 695), with one course substitution allowed by approval. Available on the Knoxville campus and via Distance Education, as either an Add-On or Stand-Alone certificate.
Why study Educational Data Science?
Education generates more data than ever: learning management systems, digital assessments, and online professional networks all leave rich digital traces. Yet most researchers and practitioners lack training to responsibly access, analyze, and interpret this complex data. Studying Educational Data Science equips you to fill that gap.
You’ll learn to wrangle messy datasets into usable formats, visualize patterns clearly, and apply statistical and machine learning models to answer meaningful questions about teaching and learning. Beyond technical skills, the program emphasizes ethics, privacy, and justice, ensuring you use data responsibly and equitably in educational settings.
Whether you’re a researcher, teacher educator, instructional designer, or policy analyst, this certificate prepares you to turn digital data into actionable insight. As demand grows for professionals who can bridge education and data science, this credential positions you to lead that work, asking better questions, building sound analyses, and using data to improve learning outcomes for students.
What can you do with a Graduate Certificate in Educational Data Science?
A Graduate Certificate in Educational Data Science opens doors across research, K-12 and higher education, and industry roles that increasingly rely on data-driven decision-making. Graduates are equipped to:
- Analyze data from learning management systems to improve course design, student engagement, and retention
- Conduct learning analytics research in academic or institutional settings
- Design and evaluate educational technology and digital learning tools
- Apply machine learning models to predict student outcomes and identify at-risk learners
- Create data visualizations that communicate insights to educators, administrators, and policymakers
- Support institutional research, assessment, and accreditation efforts with rigorous data analysis
- Advise on ethical, privacy-conscious use of student data in schools and districts
- Contribute to educational policy by grounding recommendations in evidence from large-scale datasets
- Pursue further graduate study or research (e.g., a PhD) with a strong data science foundation
The certificate is valuable whether you’re currently a graduate student adding a specialized credential to a degree program, or a working professional seeking to build technical skills without committing to a full degree. As education becomes more data-intensive, this credential positions graduates to lead in roles at the intersection of education, technology, and analytics.
Featured Courses
EDCADE 680 – Foundations of Educational Data Science
Introduces students to the data science software and programming language R. Course activities focus on preparing, using, and visualizing complex data sources for analysis using the tidyverse suite of R packages. Data ethics are foregrounded. Includes an introduction to text analysis/Natural Language Processing.
CADE 685 – Learning Analytics and Advanced Data Science Methods
Introduces advanced data science methods in learning analytics to examine how people learn in technology-rich environments. Students will explore methods such as machine learning, text analysis, network analysis, and artificial intelligence-based methods, while extending into multimodal learning analytics.
CADE 691 – Visualizing Data Using R
Intended to support students to create static visualizations (e.g., visualizations for inclusion in presentations and publications) and dynamic visualizations (e.g., those that can allow researchers and others to interact with the visualization).
STEM 695 – Capstone in Educational Data Science
Students will complete an educational data science course project involving advanced descriptive or modeling methods that can form the basis of a conference presentation proposal, journal article submission, grant proposal, or report. Includes an introduction to various techniques for creating and sharing data science products using R, such as interactive web applications (i.e., Shiny apps), dashboards, and web-based books.
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