Masters in Applied Data Science
A Multi-Disciplinary Applied Data Science Master’s Degree Program is designed to train professionals who can manage and manipulate potentially complex datasets; analyze their content; and effectively communicate these analyses’ results and significance to managers and other decision-making personnel. The program consists of a standard core curriculum that includes training in mathematics, statistics, and computer science; and the option to individualize the program with a focus in one of six possible areas. The program can be completed in 18 months online or in-person.
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:
- 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.
- Applicants should have demonstrated prerequisite knowledge in the following areas.
ETSU classes that might be used to meet each competency appear in parentheses.
- Programming – Basics of contemporary programming languages (CSCI 1250 or CSCI 1260) and object-oriented language, (e.g., Python or R)
- Data – familiarity with handling and manipulating data.(CSCI 2020)
- Calculus – Differentiation. (MATH 1910)
- Background in Linear Algebra (Matrix Algebra) is desireable (MATH 2010)
- Statistics (MATH 1530 or equivalent, 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 complete prerequisite knowledge within a specified time frame.
- Evaluation
Demonstration of Eligibility. Applicants must submit each of the following:
- Academic Record: Applicants will submit transcripts from all previously attended institutions.
- The GRE is not required.
- 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 two 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.
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:
- MATH 5257- Numerical Analysis (3)
- MATH 5810- Operations Research I (3)
- MATH 5820- Operations Research II (3)
- MATH 5890- Stochastic Modeling (3)
- STAT 5047 - Mathematical Statistics 1 (3)
- STAT 5057- Mathematical Statistics 2 (3)
- STAT 5217- Statistical Machine Learning (3)
- STAT 5287- Applications of Statistics (3)
- STAT 5307- Sampling and Survey Techniques (3)
Computation
This option focuses on:
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:
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)