Sep 27, 2023
General Program Information
Robert Price, Ph.D., Department Chair, Graduate Coordinator
312-A Gilbreath Hall
The Masters of Science in Applied Data Science is designed to train professionals to manage, manipulate and analyze potentially complex datasets and communicate these findings to managers and other personnel. The program consists of a standard core curriculum that includes training in mathematics, statistics, and computer science; a team-oriented capstone project in partnership with an industrial collaborator; and the option to individualize the program by concentrating on one of six possible focus areas.
Program Admission Requirements
- Applicants must have completed an undergraduate degree with an overall undergraduate GPA of 3.0 or higher on a 4.0 scale prior to the first semester of study. Accelerated Bachelors to Masters students may be admitted to the program before completing the Bachelors degree, but must meet all Graduate School requirements for admission to the accelerated program.
- Applicants should have demonstrated prerequisite knowledge in the following areas. ETSU classes that meet each competency appear in parentheses.
- Programming - Basics of contemporary programming languages, including state change, selection, and iteration; coding style; modular code (functions and classes); and object-oriented programming (inheritance and polymorphism). (CSCI 1250 Introduction to Computer Science I and CSCI 1260 Introduction to Comuter Science II)
- Database Systems - Creating, maintaining, and querying relational databases. (CSCI 2020 Fundamentals of Database)
- Calculus - Differentiation, integration, sequences, and series. (MATH 1910 Calculus I and MATH 1920 Calculus II)
- Linear Algebra - Systems of linear equations, matrix algebra, inner products, transformations, eigenvalues. (MATH 2010 Linear Algebra)
- Calculus-Based Probability and Statistics - Basic probability, mathematical expectation, discrete and continuous probability distributions, sampling distributions, one- and two-sample estimation, hypothesis testing, linear regression and correlation. (MATH 2050 Foundations of Probability and Statistics - Calculus Based)
Professional experience may be used to waive prerequisite coursework requirements; this is evaluated on a case-by-case basis by the program faculty. Applicants lacking prerequisite requirements may be admitted provisionally. Provisional admission could require students to take online boot camp courses in Computing and Mathematics/Statistics before the first semester of enrollment or by the end of the first semester of enrollment.
Applicants will be evaluated based on the following factors:
- Demonstration of Eligibility - Applicants must submit each of the following:
- Academic Record. Applicants will submit transcripts from all previously attended institutions.
- Resumé/Curriculum Vitae. Applicants will submit a detailed list of professional experience
- Personal Statement - Applicants will write a brief, one-page personal statement that discusses their background and the desire to pursue graduate study in Data Science.
- Recommendation Letters - Applicants should provide recommendations from at least three references. References are strongest when they are from current or former faculty members who can attest to readiness for graduate study. Professional references who can address eligibility requirements are also considered.
All students must complete and pass an oral examination in the form of a presentation to their faculty committee at the end of their culminating experience.
Applied Data Science, M.S. Degree Requirements: 39 credits
|Culminating Experience: Choose one
| Thesis + Focus Area Courses
|| (12 credits)
| Non-thesis Option: Internship + Focus Area Courses
|| (12 credits)
Students can choose whether to complete a thesis or the second part of the industrial practicum as their culminating experience.
Choose an Option:
Thesis Option: Thesis and 6 credits in one focus area. Students will choose two courses from one focus area if they are on the Thesis option.
Non-Thesis Option: Internship and 9 credits in one focus area.
Core Curriculum: 27 credits
All core courses are 3-credit courses.
Culminating Experience: 12 credits
For their culminating experience, students can choose to complete a thesis (6 credits) and 6 credits from one focus area or the second part of the industrial practicum and 9 credits from one focus area.
Choose three courses from one focus area. Subject to department approval, other courses than listed below can be selected to meet students’ interests and needs. Some focus area courses are not yet available online. Online students will be advised accordingly.
Students who chose thesis as their culminating experience will choose 6 credits from one focus area. Unless otherwise noted, all courses count for three (3) credits.
Health Science Focus Area
General Data Science Focus Area