Masters in Applied Data Science
Program Overview
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Master real-world data science skills with ETSU’s multi-disciplinary Applied Data Science program. Our MS degree trains professionals to manage and analyze complex datasets, extract insights, and communicate results that drive decision-making.
Now in its fourth year, the program integrates a strong core in Statistics and Computer Science while offering opportunities to specialize in Business, Health Sciences, Sports Science, Computation, and Theory. Students may complete the degree online, on campus, or in a hybrid format--often in as little as 18 months.
A highlight of the program is the year-long, industry-based project, where students work in teams on problems proposed by real-world partners. ETSU has collaborated with organizations such as Eastman Chemical, Chick-fil-A Corporation, Oak Ridge National Laboratory and Sandia National Laboratories, giving students valuable applied experience and networking opportunities.
Fast Facts
- Length: 18--24 months (full-time)
- Credits: 33 credit hours
- Delivery: Online, On-Campus, or Hybrid
- Tracks: Thesis | Internships
- Start Terms: Fall & Spring
These resources show the strong and growing market demand for data-driven professionals. ETSU’s Applied Data Science program equips students with the practical and analytical skills employers value most.
Robert M. Price
Graduate CoordinatorDepartment of Mathematics & Statistics
- pricejr@etsu.edu
- 423-439-6960
- Gilbreath 307-A
Prerequisites and Admission Criteria
Eligibility Requirements
Academic Background
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A completed undergraduate degree with an overall GPA of 3.0 or higher (on a 4.0 scale) prior to the first semester of study.
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Accelerated Bachelors-to-Masters students may be admitted before completing the bachelor’s degree but must meet all ETSU Graduate School admission requirements for the accelerated program.
Prerequisite Knowledge
Applicants should demonstrate foundational knowledge in the following areas. ETSU
courses that meet each competency are listed in parentheses:
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Programming: Fundamentals of a contemporary programming language (e.g., Python or R) and object-oriented concepts (CSCI 1250 or CSCI 1260).
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Data Management: Experience handling and manipulating data (CSCI 2020).
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Calculus: Differentiation (MATH 1910).
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Linear Algebra: Background in matrix algebra is desirable (MATH 2010).
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Statistics: Introductory or applied statistics (MATH 1530 or MATH 2050, or equivalent).
Application
Applicants are evaluated based on academic preparation, professional experience (if applicable), stated interest and readiness for the program, and letters of recommendation.
Applicants must submit the following:
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Academic Transcripts: Transcripts from all institutions where a degree was awarded or where graduate coursework was completed.
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Résumé or Curriculum Vitae: Detailing relevant academic, professional, and technical experience.
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Personal Statement: A brief (one-page) statement outlining your background, interests, and motivation for pursuing graduate study in Data Science.
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Letters of Recommendation: Two references are required. Recommendations from current or former faculty members are preferred; professional references who can address your readiness for graduate study are also accepted.
Funding and Financial Support
This is a self-funded program. Students are responsible for tuition and fees; however, scholarship opportunities may be available through the ETSU Graduate School. Please see Graduate School scholarships for financial assistance opportunities.
This is a self-funded program. Students are responsible for tuition and fees; however, scholarship opportunities may be available through the ETSU Graduate School. Please see Graduate School scholarships for financial assistance opportunities.