Mathematics & Statistics

Masters in Applied Data Science

Program Overview

The Masters in Applied Data Science is designed to train professionals who can manage and manipulate massive, potentially complex datasets; analyze their content; and effectively communicate these analyses’ results and significance to managers and other decision-making personnel.

Graduates with a Master's degree in Applied Data Science would qualify for job opportunities in several areas:

  • Healthcare
  • Medical Research
  • Banking and Financial Services
  • Real Estate
  • Insurance
  • Sports
  • Government and National Defense
  • Entertainment Services
  • Food Industry
  • Automotive Industry

 

Prerequisites and Admission Criteria

Eligibility Requirements:
  1. A completed 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.
    • 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 and CSCI 1260)
    • Database Systems – Creating, maintaining, and querying relational databases. (CSCI 2020)
    • Calculus – Differentiation, integration, sequences, and series. (MATH 1910 and MATH 1920)
    • Linear Algebra – Systems of linear equations, matrix algebra, inner products, transformations, eigenvalues. (MATH 2010)
    • 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)
      • Note: 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 participate in 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.
Foundation
It is expected that applicants have taken the courses listed below or have a similar foundation in knowledge before starting this program.
Evaluation

Applicants will be evaluated based on the following factors:

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
  3. 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.
  4. 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.

 

Curriculum (39 Credit Hours)

Core Courses: 30 Credits (Thesis Option: 33 Credits)

 

  • MATH 5830 - Analytics and Predictive Modeling (3)
  • STAT 5047 - Mathematical. Statistics 1 (3)
  • STAT 5710 - Statistical Methods 1: Linear Models (3)
  • STAT 5720 - Statistical Methods II: Generalized Linear Models (3)
  • STAT 5730 - Applied Multivariate Statistical Analysis (3)
  • CSCI 5000 - Data Management (3)
  • CSCI 5260 - Artificial Intelligence (3)
  • CSCI 5270 - Machine Learning (3)

Culminating Experience
 

  • Option 1: Thesis in MATH 5960 and Internship Experience in Data Science I (STAT 5910) (6)
  • Option 2: Internship Experience in Data Science I and II (STAT 5910 and 5920) (9)

Note: These could be team projects, with students serving on 2 different teams with different companies that stem across the year. 

Concentration Area: 9 Credits (Thesis Option: 6 credits)

 

Theory

This option focuses on:

Computation

This option focuses on:

  • CSCI 5010 - Programming for Data Analytics (3)
  • CSCI 5037 - Natural Language Processing and Text Analysis (3)
  • CSCI 5047 - Data Analytics and Visualization (3)
  • CSCI 5050 - Decision Support Systems (3)
  • CSCI 5620 - Analysis of Algorithms (3)

Health Sciences

This is the 3rd set of content that will appear when the tab is clicked.

Sport Science

This option focuses on:

  • PEXS 5270 - Sport Biomechancis (3)
  • PEXS 5520 - Instrumentation 

Business

 

General Data Science

 

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