Program: Data Science, (MS) - University of Memphis

Jun 27, 2025

2023-2024 GRADUATE CATALOG

Admission Requirements

A baccalaureate degree with a GPA of at least 3.0.
An acceptable score on the Graduate Record Examination (GRE) general test (verbal and quantitative) is required from within the past five years. Verbal and quantitative GRE scores at 70% or above are preferred in each area. Applicants already holding an appropriate master’s degree, or its professional equivalent may be exempted from the GRE requirement. Other professional school standardized test scores (MCAT, DAT, GMAT, or LSAT) may be substituted for the GRE by applicants who are working toward or who have already earned post-baccalaureate degrees for example, in medicine, dentistry, management, or law. Test scores must be sent directly to Graduate Admissions by the testing agency. The University of Memphis institution code number for reporting ETS scores is R-1459.
Two letters of recommendation from three individuals (at least one letter from a former professor or instructor) familiar with the applicant’s academic background or experience in Computer Science, Statistics, Mathematics and related issues, specifying in detail the applicant’s capabilities for graduate study and for future performance as a data scientist, are required.
Personal statement of approximately 750 to 1,000 words indicating his/her present interests and career goals, including how the MS-DS will prepare the candidate to achieve these goals.

International Students

For International Students and not native speakers of English there is an English proficiency requirement

All applicants who will be attending the University on a visa and who are not native speakers of English and are not graduates of the University of Memphis must supply a minimum score of 79 on the internet-based Test of English as a Foreign Language (TOEFL/iBT), 220 on the computer-based test, and 550 on the paper-based test (TOEFL/PBT). The International English Language Testing System (IELTS) will also be acceptable in lieu of the TOEFL with a minimum acceptable score of 6.0.

Prerequisites

An applicant will be expected to have the necessary mathematical, computational, and statistical background (calculus, linear algebra, programming, introductory probability, and introductory statistics).

Retention Requirements

Students must earn a grade of B (3. 0) or higher in all required courses. The MS program will adhere to Graduate School policy regarding course grades and repetition of courses. All courses applied toward MS degree program requirements must have the advisor’s written approval.

Graduation Requirements

To qualify for graduation, students must meet the following requirements: complete a minimum of 33 semester hours of graduate course work beyond the bachelor’s degree. This may include a 3-hour project course or the capstone course (students also have a Master’s Thesis option).

Academic Program Requirements

The Master of Science degree in Data Science requires completion of 33 semester credit hours as follows: 15 credits from the core courses (see below) and 18 credits from the list of electives (with the recommendation that 9 credits must be from a cluster or concentration area – see available clusters at www.memphis.edu/datascience/programrequirements.php and below) including either a Master’s project (3 credits) or a Master’s Thesis (6 credits) in which case only 12 credits are needed from the list of electives. Alternatively, students may opt for a Capstone Project course (3 credits) as a way to meet the comprehensive examination requirement of the Graduate School for students who do not write a thesis. Students may choose an Independent Study (3 credits) or Internship (3 credits) and if they opt for a Master’s project or the Capstone Project course, only 12 credits are needed from the list of electives.

Core Courses

COMP 7150 - Fundamentals of Data Science ** Credit Hours: (3)
COMP 7115 - Database Systems Credit Hours: (3)
COMP 7745 - Machine Learning ** Credit Hours: (3)
MATH 7635 - Adv Stat Learning I Credit Hours: (3)
MATH 7636 - Adv Stat Learning II Credit Hours: (3)

NOTES:

COMP 6001 - Computer Programming or equivalent may be taken as a bridge course for those with no or minimal programming background. [intro to programming is also covered in the Fundamentals of Data Science course].
MATH 6635 and MATH 6636 or equivalent may be taken as bridge courses for those with little or no statistics background.

List of Electives (students are encouraged to pick at least 3 electives from a cluster or concentration area)

Core Data Science Cluster (Cluster 1)

COMP 7/8116 - Advanced Database Systems

COMP 7/8118 - Data Mining

COMP 7/8130 - Information Retrieval/Web Search

COMP 7/8740 - Neural Networks

COMP 7/8747 - Advanced Topics in Machine Learning

COMP 7/8780 - Natural Language Processing

MATH 7/8670 - Applied Stochastic Models

MATH 7/8680 - Bayesian Inference

MATH 7/8657 Multivariate Statistics

MATH 7647 Nonparametric Statistics

MATH 7/8660 Applied Time Series Analysis

MATH 7/8685 - Simulation & Computing

MATH 7/8695 - Bootstrap/Other Methods

MATH 7/8759 - Categorical Analysis

ESCI 6515 Geographic Information Science

Biomedical Cluster (Cluster 2)

BIOL 6490: Introduction to Genomics and Bioinformatics

BIOL 7/8708: Data Science for Biologists

COMP 7/8295: Algorithms in Computational Biology and Bioinformatics

PUBH 7/8104 Large Data Sets

PUBH 7/8205: Special Topics, Mining Data

PUBH 7/8153: Biostatistics in Bioinformatics

PUBH7/8150: Biostatistical Methods I

PUBH7/8152: Biostatistical Methods II

PSYCH 7302/8302: Advanced Statistics for Psychology I

Economics Cluster (Cluster 3)

ECON 7810/8810: Econometrics I (Fundamentals of Econometrics)

ECON 7811/8811: Econometrics II (Panel and limited dependent variable methods, inter alia)

ECON 8812: Econometrics III (Times Series Analysis)

Business Information Technology Cluster (Cluster 4)

MIS 7660 Advanced Data Management

MIS 7621 Business Machine Learning II

MIS 7720 Business Artificial Intelligence

MIS 7710 Web Analytics

Engineering Cluster (Cluster 5)

CIVL 7360 - Transp Econ & Decision Making

CIVL 7012 - Prob Meth In Engr

CIVL 7263 - Intro. to Num. Opt. for Eng.

CIVL 7269 - Quantitative Approaches to Engineering Decision Making