Masters in Applied Data Science

Program Overview

Mathematics & Statistics

Program Overview

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

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

Career Opportunities in Applied Data Science

Common job titles include:

Data Scientist
Data Analyst
Machine Learning Engineer
Business Intelligence Analyst
Data Engineer
Quantitative Researcher

Typical salary ranges (U.S. Bureau of Labor Statistics and Glassdoor, 2024 data):

Data Analyst: $65,000 – $90,000
Data Scientist: $90,000 – $140,000
Machine Learning Engineer: $110,000 – $160,000
Data Engineer: $95,000 – $145,000
Business Intelligence Analyst: $70,000 – $100,000


Where to explore current opportunities:

DataJobs.com   Focused on analytics and data science roles
Outer Join – Remote data science positions
Robert Half  Salary and jobs


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.

Prerequisites and Admission Criteria

Eligibility Requirements

Academic Background

  • 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.

  • 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:

  • Programming: Fundamentals of a contemporary programming language (e.g., Python or R) and object-oriented concepts (CSCI 1250 or CSCI 1260).

  • Data Management: Experience handling and manipulating data (CSCI 2020).

  • Calculus: Differentiation (MATH 1910).

  • Linear Algebra: Background in matrix algebra is desirable (MATH 2010).

  • 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:

  • Academic Transcripts: Transcripts from all institutions where a degree was awarded or where graduate coursework was completed.

  • Résumé or Curriculum Vitae: Detailing relevant academic, professional, and technical experience.

  • Personal Statement: A brief (one-page) statement outlining your background, interests, and motivation for pursuing graduate study in Data Science.

  • 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.

M.S. Degree Requirements: 33 Credit Hours

Core Courses: 24 Credits

Thesis Option:  9 Credits

Non-Thesis Option: 9 Credits

 

Core Curriculum:

  • MATH 5830 - Analytics and Predictive Modeling (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)
  • STAT 5910 - Internship Experience in Data Science I (3)

Culminating Experience: 9 Credits

For their culminating experience, students can choose to complete a thesis (3 credits) and 6 credits from one focus area OR the second part of the industrial practicum and 6 credits from one focus area.

Thesis Option:

MATH 5960 - Thesis (3 credits) and Focus Area Courses (6 credits)

Non-Thesis Option:

STAT 5920 - Internship Experience in Data Science II (3 credits) and Focus Area Courses (6 credits)

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

Focus Area: 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 option focuses on:

  • ALHE 5150 - Population Health Issues for the Allied Health Professional (3 credits)
  • ALHE 5200 - Assessment, Planning, and Evaluation (3 credits)
  • ALHE 5500 - Methods of Research in Allied Health (3 credits)
  • BSTA 5350 - Intermediate Biostatistics (3 credits)
  • BSTA 5360 - Clinical Research: Design and Analysis (3 credits)
  • BSTA 5385 - Applied Longitudinal Data Analysis (3 credits)
  • BSTA 5390 - Survival Analysis in Public Health (3 credits)
  • BSTA 6170 - SAS Programming with Research Applications in Public Health (3 credits)
  • COBH 5250 - Community-Based Methods in Public Health (4 credits)
  • EPID 5100 - Analytic Methods in Public Health (4 credits)
  • EPID 5405 - Intermediate Epidemiology (3 credits)
  • EPID 5430 - Epidemiology of Infectious Disease (3 credits)
  • EPID 5460 - Environmental Epidemiology (3 credits)
  • EPID 5480 - Genetic Epidemiology (3 credits)
  • EPID 6410 - Advanced Multivariate Epidemiologic Data Analysis (3 credits)
  • EPID 6420 - Applied Epidemiologic Analysis (3 credits)
  • EPID 6470 - Risk Behavior Epidemiology (3 credits)
  • HSMP 5040 - Health Systems, Regulations, and Policies (4 credits)
  • HSMP 5300 - Quality Improvement in Health Services Organizations (3 credits)
  • HSMP 6310 - Population Health Management (3 credits)
  • HSMP 6320 - Health Services Research Methods (3 credits)
  • MATH 5880 - Modeling of Infectious Diseases and Social Networks (3 credits)
  • NRSE 6030 - Quantitative Methods in Nursing Research (3 credits)
  • NRSE 6035 - Advanced Quantitative Design and Data Analysis in Nursing Research (3 credits)
     

Sport Science

This option focuses on:

  • PEXS 5270 - Sport Biomechancis (3)
  • PEXS 5520 - Instrumentation in Exercise and Sport Science (3)
  • PEXS 5670 - Research Design and Analysis (3)
  • SALM 5670 - Sport Management Research (3)

Business

This option focuses on:

  • AMBA 5140 - Data Analysis and Modeling (3)
  • ACCT 5150 - Accounting Information for Decision Making (3)
  • MKTG 5717 - Data Driven Marketing Decisions (3)
  • MSDM 5010 - Digital Marketing Research (3)
  • MSDM 5050 - Web Analytics (3)
  • MSDM 5060 - Business Analytics, Data Visualization and Online Metrics (3)
  • MSDM 5080 - Search Marketing (3)
  • MSDM 5090 - Digital Marketing Strategy (3)
  • MSDM 5100 - Digital Marketing Strategic Experience (3)
     

General Data Science

This option focuses on:

  • BIOL 5367 - Modeling Biological Systems (3)
  • BIOL 5500 - Biometry (3)
  • CJCR 5950 - Quantitative Methods in Criminology (3)
  • EDFN 5950 - Methods of Research (3)
  • ELPA 6300 - Professional Needs of Individuals and Groups (6)
  • ELPA 6870 - Field Research in Educational Leadership (3)
  • ELPA 6951 - Seminar in Research Analysis and Interpretation (3)
  • ELPA 6952 - Action Research (3)
  • GEOS 5010 - Geospatial Analysis (3)
  • GEOS 5017 - Advanced Cartography: Web & Mobile Mapping (3)
  • GEOS 5237 - Advanced Remote Sensing (3)
  • GEOS 5300 - Topics in Geospatial Analysis (3)
  • GEOS 5317 - Advanced Geographic Information Systems (3)
  • GEOS 5320 - Geographic Information Systems Projects (3)
  • GEOS 5350 - Statistics for Geosciences (3)
  • GEOS 5807 - Unmanned Aerial Systems (UAS) Mapping and Modeling (3)
  • PHYS 5007 - Computational Physics (4)
  • PSYC 5210 - Statistical Methods (3)
  • PSYC 5410 - Correlation and Multiple Regression (3)
  • PSYC 6210 - Meta-Analytic Research Methods (3)
  • PSYC 6410 - Covariate Structural Modeling (3)
  • SOCI 5444 - Data Analysis (3)