Mar 21, 2023
The PhD with a major in management science is designed to prepare students for research related to the application of analytical tools to complex decision making. Three primary objectives of the program are:
- To provide, through relevant coursework, a thorough knowledge of common analytical models and their uses in business.
- To provide sufficient advanced study in a supporting area to qualify the graduate for a faculty position in management science, business analytics, or any supporting area. The candidate may choose from the business functional areas (accounting, finance, marketing, management, and supply chain management) or other disciplines (e.g., computer science, forestry, ecology, and public administration).
- To develop in the student, through coursework in management science, statistics, operations management, and computer science, a high degree of analytical maturity to enhance a potential career in management, research, or teaching.
The PhD concentration in analytics will focus on research specifically aimed at applying analytical tools for modeling and analyzing data to support decision making in complex real-world systems in business and industry.
- In addition to any other admission requirements for the Graduate School:
- Submit online graduate application to the Office of Graduate Admissions.
- Submit three recommendation forms.
- Submit a GRE or GMAT score.
- Submit candidate statement of purpose.
- Prerequisites for Management Science Courses. The management science program is interdisciplinary and students in other degree programs are encouraged to enroll in management science courses. Course prerequisites are designed to indicate the level at which courses are taught. Interested students whose prior coursework does not match the prerequisites are encouraged to seek the instructor’s guidance and consent to enroll.
Credit Hours Required
- 72 graduate credit hours beyond a bachelor’s degree
- 48 graduate credit hours beyond a master’s degree
- Minimum Courses (12 credit hours)
- Applied Area (12 credit hours)
- Normally selected from ECON, INMT, FINC, MARK, etc. and in consultation with the major professor.
- For the Analytics Concentration, three courses are selected from the following for 12 credit hours.
- MGSC 600 (24 credit hours)
- This effort, which is beyond the minimum 48 credit hours of coursework, normally is completed in the third year of the program.
Additional Course Requirements
- A minimum of 48 credit hours of coursework taken for graduate credit (exclusive of thesis or dissertation) is required.
- Some of the credit hours may be the coursework from a master’s program, although a master’s is not a prerequisite for the doctorate.
- The candidate must complete a minimum of 24 credit hours of PhD coursework (work beyond the masters) at the University of Tennessee, Knoxville.
- At least 18 credit hours of the PhD coursework must be at the 600-level.
- Above two requirements are exclusive of thesis or dissertation credit hours.
- Entering students who have completed graduate studies in applicable fields will be granted course credits for work that is equivalent to required courses in the program.
- Qualifying Examination
- The student must demonstrate mastery of the core methodological foundation of data science by passing a written qualifying examination.
- These requirements generally are completed by the end of the first year of the program.
- There is no foreign language requirement.
- Comprehensive Examination
- Prior to admission to candidacy for the degree, and normally after completion of the second year of the program, the student must pass a written comprehensive examination covering the theory of data science as applied to their research focus.
- Topics included in this examination are determined on an individual basis.
- Students will be expected to demonstrate an integrative ability that goes beyond simple mastery of course content.
- A final oral examination is conducted over the dissertation and such other segments of the program that the faculty committee deems appropriate.