Online Graduate Certificate: Educational Data Science
College of Education, Health, & Human Sciences
Program Overview
Are you interested in applying digital data collection, analysis, visualization, and other data science skills in an educational setting? The University of Tennessee, Knoxville offers an online Graduate Certificate in Educational Data Sciences for current UT graduate students or as a stand-alone certification. As our world becomes more data-rich everyday, educators need strategies for working and engaging with complex sources of data. Gain a broad knowledge and skill base at UT through this fully online graduate certificate program.
Credit Hours
12
Cost Per Credit Hour*
In-State $700
Out-of-State $775
Modality
Synchronous
Admission Terms
Fall, Spring
*Cost per credit hour is an estimate based on maintenance and university fees. Some programs may have additional course fees. Please contact your department for additional information on any related fees, and visit Tuition and Fees in Detail at One Stop.
Acquire the Skills to Be a Leader in Educational Data Science
In an increasingly data-rich world, educators are often looking for strategies to engage with complex sources of data. The Educational Data Science graduate certificate is designed to provide a broad knowledge and skill base of multiple facets of data science, ranging from discussing ethics and privacy issues related to the context of data science to creating static and dynamic data visualizations using R. Students in this graduate certificate program will explore ethical applications of machine learning in education and also gain experience with conducting research that relies on digital data sources.
Featured Courses
A total of 4 graduate courses and a capstone project are required for the certificate. Students may substitute one of the four required courses with a related graduate course approved by the Educational Data Science Graduate Certificate program coordinator.
STEM 680: Foundations of Educational Data Science I
Intended to support graduate-level students to be able to apply data science methods to topics of teaching, learning, and educational systems. Introduces students to the data science software and programming language R. Course activities focusing 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. No pre-requisites or programming experience is required.
STEM 685 – Foundations of Educational Data Science II
Intended to support graduate students to use data science methods to study new technology-based environments, such as online courses, educational technology platforms, and social media-based networks. Advanced data visualization and social network analysis techniques are emphasized. More advanced methods around writing custom functions and using machine learning for analyzing complex data sources are introduced. Course involves the use of the statistical software R.
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. Students will receive ongoing feedback and support related to their course project throughout the semester, culminating in sharing their work in presentations open to the public.
STEM 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). Will use educational examples and data sets but is open to students across programs. Course involves the use of the statistical software R.
