Ph.D. in Analytics, Information and Operations
Program information
Faculty research is often motivated by real-life problems faced by firms in diverse industry sectors such as military, airlines, television, technology, digital marketing, social networking, insurance and healthcare.
Analytics, information, and operations faculty are highly regarded for their research productivity and placement of doctoral graduates.
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Research
Students begin their own research during the first year of the program and often present to faculty and other doctoral students early in their second year. Many of these papers are eventually published in academic journals.
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Teaching
Students will teach an undergraduate class usually in their second or third year of the program. Students typically teach three undergraduate classes before graduating.
Program details
Core courses
BSAN 920: Probability for Business Research OR Equivalent Course
BSAN 921: Statistics for Business Research OR Equivalent Course
ECON 800: Optimization Techniques I
ECON 801: Microeconomics I
ECON 802: Microeconomics II OR ECON 830: Game Theory and Industrial Organization
ECON 817: Econometrics I
Concentration courses
BSAN 922: Advanced Regression
BSAN 923: Stochastic Processes OR MATH 865: Stochastic Processes I
BSAN 935: Analytical Research in OM OR BSAN 924: Seminar in Machine Learning OR IST 995: Seminar in IS OR BSAN 925: Empirical Methods in OM or BSAN 926: Seminar in Research Methods
Supporting courses
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. Choose six courses from the list below.
ECON 790: Game Theory and Applications
ECON 818: Econometrics II
ECON 830: Game Theory and Industrial Organization
ECON 916: Advanced Econometrics II
EPSY 906: Latent Trait Measurement and Structural Equation Models
EPSY 908: Structural Equation Modeling II
FIN 821: Corporate Finance
FIN 830: Investments
FIN 832: Derivatives and Risk Management
MATH 765: Mathematical Analysis I (recommended)
MATH 790: Linear Algebra II (recommended)
MKTG 952: Introduction to Marketing Models
MKTG 954: Pricing and Strategy
Course(s) from potential new doctoral course offerings: Multivariate Analysis, Data Mining, Hierarchical Modeling, Structural Equation Modeling, etc.
For more information, view a detailed list of courses in the academic catalog.
Area of Concentration
Most students admitted in analytics, information, and operations 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, 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.
Supporting Areas
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 analytics and operation students are marketing, economics, finance, etc. Courses recommended for preparation for the qualifiers may not be included in satisfying the supporting area requirement.
Research Methodology
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 analytics and operations 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.
Year 1
Coursework and research
Year 2
Coursework and research
Year 3
Comprehensive exams and research
Year 4
Dissertation proposal and job market
Year 5
Dissertation defense
Note: Some students complete the program in four years.
