Online Graduate Certificate in Educational Data Science
Educational Data Science
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 every day, 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.
Become a Leader in Educational Data Science
Educators in an increasingly data-rich world are often looking for strategies to engage with complex data sources. 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.
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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.
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.
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 for writing custom functions and using machine learning to analyze complex data sources are introduced. The course involves the use of the statistical software R.
Intended to support students in creating 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. The course involves the use of the statistical software R.
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. The course also includes an introduction to various techniques for creating and sharing data science products using R, such as interactive web applications (e.g., Shiny apps), dashboards, and web-based books. Students will receive ongoing feedback and support for their course project throughout the semester, culminating in sharing their work in presentations open to the public.
