Applied Data Science, M.S.

General Program Information

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

Eligibility Requirements:

1. 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.
2. Applicants should have demonstrated prerequisite knowledge in the following areas. ETSU classes that meet each competency appear in parentheses.
   1. 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)
   2. Database Systems - Creating, maintaining, and querying relational databases. (CSCI 2020 Fundamentals of Database)
   3. Calculus - Differentiation, integration, sequences, and series. (MATH 1910 Calculus I and MATH 1920 Calculus II)
   4. Linear Algebra - Systems of linear equations, matrix algebra, inner products, transformations, eigenvalues. (MATH 2010 Linear Algebra)
   5. 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:

1. Demonstration of Eligibility - Applicants must submit each of the following:
   1. Academic Record. Applicants will submit transcripts from all previously attended institutions.
   2. Resumé/Curriculum Vitae. Applicants will submit a detailed list of professional experience
2. 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.
3. 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.

Graduation Policy

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

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.

• CSCI 5000 - Data Management (3 credits)
• CSCI 5260 - Artificial Intelligence (3 credits)
• CSCI 5270 - Machine Learning (3 credits)
• MATH 5830 - Analytics and Predictive Modeling (3 credits)
• STAT 5047 - Mathematical Statistics I (3 credits)
• STAT 5710 - Statistical Methods I (3 credits)
• STAT 5720 - Statistical Methods II (3 credits)
• STAT 5730 - Applied Multivariate Statistical Analysis (3 credits)
• STAT 5910 - Internship Experience in Data Science I (3 credits)

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.