The Bredesen Center for Interdisciplinary Research and Graduate Education offers a graduate program leading to the Doctor of Philosophy (PhD) degree in Data Science and Engineering (DSE). This interdisciplinary degree is a collaborative effort supported by selected faculty from various colleges at the University of Tennessee, Knoxville, the UT Health Sciences Center, the University of Tennessee, Chattanooga, and research staff of Oak Ridge National Laboratory. These research and educational leaders are appointed as faculty members of the Bredesen Center for Interdisciplinary Research and Graduate Education. Members of the Bredesen Center faculty determine the curriculum and serve as the primary resource for the teaching, research, and mentoring of the students admitted to the program. The Bredesen Center Graduate Admissions Committee makes decisions on admissions, transfer, evaluation, and continuation of graduate students in the program.
In order to be admitted to the PhD program in data science and engineering, student applicants must fulfill the general admission criteria for the Graduate School of the University of Tennessee Knoxville. In addition, the student must have a Bachelor of Science degree in either engineering or a scientific field (e.g., analytics, biology, chemistry, computational science, mathematics, physics, statistics, etc.), or the equivalent. Students with other undergraduate degrees may also be admitted on a case-by-case basis by the Bredesen Center Graduate Admissions Committee. Dependent on the student’s background, additional coursework may be required to satisfy co- and prerequisites.
Credit Hours Required
A minimum of 72 graduate credit hours is required beyond the bachelor’s degree, exclusive of credit for an MS thesis.
- DSE 600 - Doctoral Research and Dissertation (36 credit hours)
- 6 credit hours of 600-level coursework at UT
Core Curriculum for Data Science (21 credit hours) selected from:
- DSE 511 - Introduction to Data Science and Computing I
- DSE 512 - Introduction to Data Science and Computing II
- DSE 537 - Introduction to Data Analysis and Data Mining
- MATH 525 - Statistics I
- STAT 563 - Probability and Mathematical Statistics
- MATH 526 - Statistics II
- MSE 510 - Mathematical and Numerical Problem Solving Skills for Materials Scientists and Engineers
- BZAN 645 - Machine Learning for Business Research
- BZAN 646 - Modern Multivariate Techniques
Note: The above courses may be substituted, if approved in advance.
Knowledge Breadth Curriculum (6 credit hours) selected from the following areas:
- Political, social, legal, ethical, and security issues related to data issues (e.g., POLS, PHYS, ESE)
- Entrepreneurship, leadership, and management (e.g., IE, ME, MGT, ESE)
Knowledge Specialization Curriculum for Domain Science (6 credit hours) select from participating departments and approved by the Bredesen Center’s Assistant Director for Data Science related to the following research areas:
- Health and Biological Sciences (e.g., CBE, MICR, BCMB)
- Advanced Manufacturing (e.g., CHEM, MSE, CBE)
- Materials Science (e.g., MSE, PHYS)
- Environmental and Climate Science (e.g., GEOL, MICR, BCMB, EEB, ESS, FORS, GEOL, LFSC, MICR, PLSC, ENVE, FWF)
- Transportation Science (e.g., CBE, CE, ECE, ME)
- National Security (e.g., COSC, ECE, POLS)
- Urban Systems Science (e.g., GEOG, ECE)
- Advanced Data Science (e.g., MATH, COSC, STAT)
- Additional courses may be selected in consultation with the major professor or research advisor
- DSE 599 - Seminar (taken three times, 1 credit hour each)