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
- 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 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.
It is expected that applicants have taken the courses listed below or have a similar foundation in knowledge before starting this program.
- CSCI 1250 - Introduction to Computer Science 1
- CSCI 1260 - Introduction to Computer Science 2.
- CSCI 2020 - Intro. to Databases.
- MATH 1910 - Calculus 1
- MATH 1920 - Calculus 2
- MATH 2010 - Linear algebra
- MATH 2050 - Calculus-Based Probability and statistics
- Familiarity with Python and R.
Evaluation
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.
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:
- MATH 5257- Numerical Analysis (3)
- MATH 5810- Operations Research I (3)
- MATH 5820- Operations Research II (3)
- MATH 5890- Stochastic Modeling (3)
- STAT 5057- Mathematical Statistics 2 (3)
- STAT 5217- Statistical Machine Learning (3)
- STAT 5307- Sampling and Survey Techniques (3)
- STAT 5287- Applications of Statistics (3)
Computation
This option focuses on:
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