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Dec 21, 2024
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2024-2025 GRADUATE CATALOG
Data Science, (MS)
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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 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.
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.
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