Computer Science, Data Science, M.S.

Print Degree Planner (opens a new window) |

Program Overview

The Department of Computer Science offers a Master of Science degree in Data Science. The M.S. in Data Science program integrates computer science, applied mathematics, and statistics to prepare graduates for careers in data science. The program is designed to be fully online, with optional on-campus immersion experiences. Upon completing the program, students gain the right blend of theoretical knowledge and practical skills in computer science and applied mathematics, enabling them to extract insights and information from large datasets. The program is ideal for students seeking advanced credentials for employment in various areas of data science. It is also well-suited for those planning to pursue a doctoral degree in data science or related fields. The Master of Science in Data Science program is offered online to accommodate the needs of working IT professionals. The M.S. in Data Science program offers a non-thesis option only. All students are required to complete a total of 30 credit hours, which include 12 credit hours of core courses, 15 credit hours of elective courses, and a 3-credit-hour design-based capstone course.

Admission Requirements

All students seeking to enroll in graduate study must apply through the Office of the Dean of the Graduate School. To be admitted to the M.S. in Data Science program, applicants should demonstrate readiness to succeed in graduate-level coursework by meeting the following criteria:

1. Bachelor’s Degree: Applicants must hold a bachelor’s degree from an accredited institution with a minimum cumulative grade point average (GPA) of 2.75 on a 4.0 scale.

2. Letters of Recommendation: Applicants must submit two letters of recommendation that speak to their academic readiness and potential for success in a graduate program.

3. Evaluation Process: Application materials will be reviewed by an admissions committee to assess the applicant’s academic background, experience, and overall preparedness for the program.

Retention Requirements

The following are the retention requirements for the M.S. in Data Science program:

1. Students must maintain a cumulative GPA of 3.00 throughout the program. After completing nine semester hours of graduate coursework, if a student’s cumulative GPA falls below 3.00 at the end of any given semester, the student will be placed on academic probation. If the student fails to raise their cumulative GPA to 3.00 in the following semester of enrollment, they will be suspended from the program.

2. Students must earn a grade of B or better in all core courses and may not receive more than two grades of C in other courses used to satisfy degree requirements.

3. Students who repeat a core course and fail to earn a B or higher will be dismissed from the program.

4. The maximum time allowed to complete the master’s degree is six calendar years. All degree requirements must be fulfilled within six years of the student’s initial enrollment in a graduate-level course.

5. Students who are dismissed from the program will not be eligible for readmission.

Important Dates

Application Deadlines

Fall Semester July 1st
Spring Semester November 1st
Summer Semester April 1

Registration Deadlines

Fall Semester Feb 15 - Apr 12, 2025
Maymester March 10-May 5, 2025
Summer Semester Mar 23 - Apr 27, 2025
Spring Semester Oct. 23, 2025 - Jan. 9, 2026

Program Duration

Usually, a student needs about four (4) semesters to complete the coursework. Most students can finish within these two years, but sometimes, students need to take one or two semesters more to finish their study.

Transfer of Credits

At the master’s level, a student may transfer a maximum of twelve (12) semester hours or eighteen (18) quarter hours of graduate credit. At the specialist’s and doctoral level, a maximum of six (6) semester hours may be transferred. Only courses in which the student earned grades “B” or better, and which are taken within the degree program time limit, will be considered for transfer.

Degree Requirements

The Master of Science in Data Science degree requires the following:

1. Admission to candidates for all degree-seeking students after the completion of nine (9) graduate credit hours in residence at the University. Students must submit an Admission to Candidacy form to the Graduate School prior to the semester in which they intend to graduate.

2. A minimum of 30 credit hours of graduate coursework.

Accreditation Information

Master of Science in Data Science program is a non-accredited degree. Non-accreditable graduate programs in Tennessee must undergo either an academic audit or external peer review on a pre-approved review cycle. Program Review Rubric lists the criteria used to evaluate a program. This rubric consists of 32 criteria. The Tennessee Higher Education Commission (THEC) uses these criteria to assess standards and distribute points to graduate programs. A self-study report is usually prepared to comprehensively review the Data Science (M.S.) degree program in the College of Engineering at Tennessee State University (TSU).

Career Opportunities

Most of the students who graduated from the M.S. program secure jobs in the data science industry. Some students continue in the PhD programs.

Faculty Credentials

All faculty who teaches in the College of Engineering meet the SACSCOC guidelines for credentials, per review of their transcripts and credentials by the College of Engineering and the Division of Academic Affairs. They hold Ph.D. degrees from respected institutions in areas related to the concentrations they teach. They have also been reviewed by the Graduate Council and certified as Graduate Faculty. Further, as all the CS undergraduate degree programs are accredited by ABET, these faculty also possess the credentials deemed appropriate for their respective programs at the undergraduate level.

Program Website

https://www.tnstate.edu/data/

Program Coordinator/Contact Information

Tamara Rogers, Ph.D., Interim Chair and Associate Professor
Office: McCord Hall 005R
Phone: 615-963-1520
trogers3@tnstate.edu

Ali Sekmen, Ph.D.2, Professor and Program Coordinator
Office: McCord Hall 005P
Phone: 615-963-5712
asekmen@tnstate.edu

Program of Study and Credit Hours Required

CORE COURSE REQUIREMENTS (12-credit hours)

DATA 5050 - Mathematics for Data Science 3 CIP:
DATA 5100 - Programming for Data Science 3 CIP:
COMP 5850 - Data Visualization (3) CIP:
COMP 6200 - Machine Learning (3) CIP:

Elective Courses - 15 Credit Hours

Students are required to complete 15 credit hours of elective coursework selected from the following list. This is a suggested list; however, students may take other graduate-level courses with the approval of their advisor and the Program Coordinator.

DATA 5200 - Statistical Learning 3 CIP:
DATA 5300 - Data Mining 3 CIP:
DATA 5400 - Algorithms for Data Science 3 CIP:
DATA 5500 - Business Data Analytics (3) CIP:
DATA 5900 - Special Topics 3 CIP:
DATA 6100 - Natural Language Processing 3 CIP:
DATA 6150 - Applied Deep Learning 3 CIP:
DATA 5350 - Applied Statistics for Data Science. 3 CIP:
COMP 5400 - Hybrid and Relational Databases (3) CIP:
COMP 5800 - Introduction to Bioinformatics (3) CIP:
COMP 6400 - Distributed Algorithm and Data Analysis 3 CIP:
COMP 6800 - Introduction to Computer Vision (3) CIP:

Note:

Courses other than this list can still be taken with the approval of advisor and program coordinator.

Capstone Design Project - 3 credit hours