Applied Data Analytics
Department of Educational Psychology
Designed For
The Applied Data Analytics master’s degree program is designed to equip students and professionals with the skills to analyze real-world data and apply those insights to drive innovation in education, EdTech and the health sector. Delivered in a fully online, flexible format, the program is perfect for those who want the flexibility to advance their education on their own schedule.
Career Objective
Graduates will be prepared to:
- Analyze complex data and translate it into actionable insights.
- Transform data into meaningful visualizations to communicate data technical and non-technical audiences.
- Evaluate the effectiveness of programs, policies and interventions.
- Apply ethical, secure and responsible data practices.
- Use data to drive impact and innovation in health and education, such as identifying learning gaps, service needs and access challenges.
At a Glance
Program Starts: Fall 2027
Application Opens:
August 2026
Deadline to Apply:
January 10
Length of Program:
24 months
Program Location: Online
Program Cost: $700 per credit hour
GRE Required? No
Lead with Analytics. Transform Outcomes.
Data‑driven roles are expanding rapidly nationwide, making advanced analytic skills more essential than ever. With education and health organizations under increasing pressure to boost outcomes and reduce costs, the ability to turn data into smart, strategic action has never been more valuable.
Our fully online, flexible master’s degree program provides the same rigorous curriculum and faculty expertise as an in-person experience, while giving you the flexibility to learn from anywhere and balance graduate study with professional and personal commitments.
- Learn from anywhere while maintaining your current career.
- Complete the degree in as little as two years.
- Gain hands-on experience with real-world data and analytics tools.
- No thesis required.
Program Details
Application Requirements
Follow the application steps outlined on our How to Apply page to submit academic transcripts, a statement of purpose, letters of reference and a resume.
International students will need to submit TOEFL scores or other evidence of language proficiency. Check the Graduate School’s website for specific requirements.
Applications open August 1st, with a January 10th deadline for a Fall program start. Admissions decisions are typically sent by late February.
Courses
The program consists of 10 courses that are designed to build from foundational analytic skills up to advanced, applied practice.
Students begin by developing core competencies in data literacy, statistical reasoning and analytic methods, then progress to hands‑on coursework focused on analyzing real‑world educational and health data.
Advanced courses integrate analytic techniques with decision‑making and program evaluation, preparing graduates to communicate insights effectively and apply data analytics with confidence in professional settings.
Students who successfully complete the program will earn a Master’s of Science in Educational Psychology – Applied Data Analytics.
Fundamentals of Probability and Statistics: Covers the basics of probability theory and statistical methods. Key concepts include an introduction to data and statistics, probability fundamentals, statistical inference and an introduction to parametric and non-parametric methods.
Data Analytics: Fundamentals and Ethics: Introduces data analytic techniques and discusses ethical considerations in data analytics. Topics include an introduction to data analytics, data collection and preparation, exploratory data analysis techniques, predictive analytics, communicating results and ethical considerations in data analytics.
Data Wrangling and Preprocessing: Practice techniques for cleaning, transforming, and preparing data for analysis using modern programming tools, including R and Python. Topics include an introduction to data analysis environments and programming fundamentals, data import and export, data cleaning, data transformation, merging and joining datasets, data preprocessing, working with text and string variables, automating data processing workflows and visualizing data throughout the wrangling process.
Data Visualization: Create informative and effective visual representations of data, emphasizing effective communication of data insights. Key topics include principles of data visualization, visualization libraries, customization and aesthetics of visualizations, interactive dashboards, visualization of multivariate, categorical, and longitudinal data, exploratory data analysis through visualization, and effective data storytelling.
Regression Methods: Introduces students to various regression methods used in applied data analytics, including linear regression, logistic regression, advanced regression techniques, generalized linear models, interactions, model evaluation and validation, and communicating results.
Machine Learning for Applied Research: Explores machine learning techniques for applied research in educational and social sciences and practices the techniques using empirical data in R. It discusses supervised learning methods such as regression, classification, and ensembles as well as unsupervised learning models for dimensionality reduction and clustering.
Applications in Data Mining: Teaches students how to apply data mining techniques and tools to discover patterns and insights in large datasets. Applications in classification techniques, regression techniques, clustering algorithms, association rule mining and anomaly detection.
Social Network Analysis in Applied Research: Examines methods for analyzing social networks, understanding relationships and structures within data collected from social interactions. The course focuses on key social network analysis (SNA) concepts, representation and construction, structure and topology, measures in SNA, visualization with SNA, SNA research applications and communicating results effectively.
Longitudinal Data Analytics: Introduces methods for analyzing data collected over time, including linear mixed-effects models, generalized linear models, autoregressive models, and survival analysis. Additional topics include an introduction to longitudinal data and data structures, data preprocessing, advanced topics, model checking and selection, applications and ethical considerations.
Fundamentals of Text Analytics: Introduces techniques for working with and analyzing textual data to extract meaningful information. Topics include text representation, exploratory analysis for text, sentiment, text classification, topic modeling, named entity recognition, text analytics for information retrieval and natural language processing (NLP).
Frequently Asked Questions
What are the classes like?
All courses are offered online and typically consist of a combination of video or podcast lectures, readings, discussions, quizzes and projects. The courses may vary from professor to professor, but all courses are delivered asynchronously, meaning students will have the flexibility to work through course modules on their own schedules.
Typically, courses are structured so that one module is covered per week. All coursework for the module must be completed within the assigned week.
How much is tuition?
The cost of tuition is $700 per semester credit hour. The program consists of 10 3-credit hour courses, so the total tuition cost is $21,000.
Am I eligible for financial aid?
Option III Students are not be eligible to receive federal financial aid. For additional information, contact the Financial Aid office. At this time, we do not offer merit-based scholarships.
Veterans can use GI Bill tuition benefits toward tuition. However, students in the online program are not eligible for the Hazelwood exemption. For more details, or to discuss your particular situation, contact the Dean of Students office.
Do I have to complete an internship?
No. An internship is not part of the program.
Will I have access to the library? Campus recreation? UT football tickets?
You have access to all of the same resources as students who are in residence, including the library, campus recreation and other on-campus amenities. You are also eligible to purchase the Big Ticket, which is the student pass for athletic events.
Will I get to participate in UT’s graduation ceremony? Will my diploma say that the degree was offered online?
The diploma received is the same as our on-campus students. You are eligible (and encouraged) to participate in the graduation ceremony on campus. Graduation exercises are held at the end of the spring semester. In order to graduate, you must have completed all degree requirements by the spring of the given year.
Faculty
Interested in statistical models with a focus on deriving and evaluating multilevel model extensions and meta-analysis models for educational, behavioral, social and medical science data.
Statistical methods and techniques for meta-analysis with a focus on selective reporting and publication bias, effect size calculations, multilevel modeling
Accepting new students
Interests include the development and dissemination of computerized adaptive testing applications in educational and psychological testing and patient-reported outcome measurements.
Accepting new students
Studies statistical and computational methods for analyzing psychometric data. Research areas include item response theory, response time modeling, cognitively diagnostic models, computer-adaptive testing, and multimodal analytics of cognitive and be...
Accepting new students
Statistical methods for causal mediation analysis, causal inference with machine learning methods, longitudinal or clustered data analysis, study design (e.g., statistical power analysis)
Accepting new students
My principal methodological research interest deals with the various facets of model specification, including, but not limited to, model comparison/selection and model modification methods. With the use of simulation techniques, I examine the perform...
Questions?
