Faculty and doctoral students in Decision Sciences are a community of scholars dedicated to producing high impact research. Their interests are reflected in the diverse mix of research topics they pursue. These research interests include supply chain management, uncertain reasoning, business analytics, time series analysis and forecasting, Bayesian statistics, lean philosophy, and production and operations management. Faculty research is often motivated by real-life problems faced by firms in diverse industry sectors such as military, airlines, television, technology, digital marketing, and social networking. For further details, please explore the Decision Sciences faculty profiles. Decision Sciences faculty are highly regarded for their research productivity and placement of doctoral graduates. They have published in journals such as Management Science, Marketing Science, Operations Research, Manufacturing and Service Operations Management, Production and Operations Management, Decision Analysis, Journal of the Operational Research Society, European Journal of Operational Research, and Artificial Intelligence.
1. DSCI 920: Probability for Business Research (or an equivalent course)
2. DSCI 921: Statistics for Business Research (or an equivalent course)
3. BE 917: Advanced Managerial Economics (or an equivalent course)
OR ECON 700: Microeconomics Theory
4. MATH 765: Intro to Theory of Functions I (or an equivalent course)
5. MATH 790: Linear Algebra II (or an equivalent course)
6. ECON 715: Elementary Econometrics, or DSCI 922: Advanced Regression (or an equivalent course)
Area of Concentration
7. DSCI 740: Time Series Analysis
8. DSCI 740: Uncertain Reasoning
9. DSCI 740: Optimization
10. SCM 998: Game Theory & Applications
11. SCM 998: Empirical Research Methodology
A minor concentration typically consists of two or more additional courses from the following list, plus two or more courses from a second concentration area. Alternatively, a minor concentration requires four or more additional courses from the following list if there is no second concentration area.
- ECON 817: Econometrics I
- ECON 818: Econometrics II
- ECON 916: Advanced Econometrics II
- FIN 705: Investment Theory
- FIN 706: Investment Analysis
- FIN 740: Forwards, Futures, and SWAPs
- FIN 741: Options
- FIN 745: Business Investments
- FIN 746: Business Financing
- MKTG 950: Advanced Marketing Research
- MKTG 952: Introduction to Marketing Models
- MKTG 954: Pricing & Strategy
- MKTG 955: Product Management
- PRE 906: Structural Equation Modeling I
- PRE 908: Structural Equation Modeling II
Program Requirements and Information
Area of Concentration
Most students admitted in decision science typically will select that area as their concentration. However, an aspirant, with the assistance of his or her faculty advisor and the area faculty, may propose an interdisciplinary area of concentration that is a combination of the traditional business disciplines of accounting, information systems, finance, human resource management, marketing, organizational behavior, and strategic management. An aspirant may also propose an interdisciplinary area of concentration that includes emphases such as international business, law, and economics. The aspirant must take at least five advanced courses in the area of concentration. These courses may include those offered outside the School of Business. Examples of courses taken by PhD students include: DSCI 740: Times Series Analysis DSCI 740: Uncertain Reasoning DSCI 935: Optimization
Coursework in the area of concentration is supplemented and strengthened by study in one or two supporting areas. A supporting area is one that supplements and complements the area of concentration. The aspirant will satisfy the supporting area requirement by taking at least four advanced courses in the supporting areas (at least two courses in each of two supporting areas, or at least four courses in one supporting area). The typical supporting areas for decision science students are marketing, economics, finance, etc. Courses recommended for preparation for the qualifiers may not be included in satisfying the supporting area requirement.
For successful qualifier assessment, the student’s program of study should include adequate preparation in research methodology. A sound research is always grounded on sound methodology. A doctoral student in decision science has the opportunity to develop methodological skill in probability and statistics, optimization, uncertain reasoning, game theory, and econometrics. A typical doctoral dissertation often utilizes one or more of the following research methodology: empirical, analytical, behavioral, and computational.
Degree Completion Timeline
Years 1-2: Coursework* Year 3: Comprehensive Exams Year 4: Dissertation Proposal Year 5: Dissertation Defense (Some students can complete the program in four years.)
Over the past several years, our PhD graduates have been placed at schools such as Virginia Military Institute, Albany State College, University of Tampa, Duke University, University of Nebraska, Omaha.