Ph.D. in Analytics 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 and operations faculty are highly regarded for their research productivity and placement of doctoral graduates.

Application deadlines

  • Priority: January 10, 2023

  • Final: February 1, 2023



  • 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.


  • 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 701: Survey of Macroeconomics OR ECON 790: Game Theory and Applications OR ECON 830: Game Theory and Industrial Organization

ECON 715: Elementary Econometrics OR BSAN 922: Advanced Regression

MATH 765: Mathematical Analysis I OR Equivalent Course

MATH 790: Linear Algebra II OR Equivalent Course

Concentration courses

BSAN 740: Optimization and Perspective Analytics OR BSAN 750: Data Mining & Machine Learning

BSAN 934: Seminar in Probability and Statistics:_______

BSAN 935: Analytical Research in OM

SCM 995: Doctoral Seminar in Machine Learning OR IST 995: Seminar in IS

SCM 998: Independent Study for Doctoral Students: Empirical Methods in OM

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.

FIN 710: Investments I

FIN 711: Investments II

FIN 712: Business Investment

FIN 713: Business Financing

ECON 790: Game Theory and Applications

ECON 800: Optimization Techniques I

ECON 817: Econometrics I

ECON 818: Econometrics II

ECON 830: Game Theory and Industrial Organization

ECON 916: Advanced Econometrics II

MKTG 952: Introduction to Marketing Models

MKTG 954: Pricing and Strategy

EPSY 906: Latent Trait Measurement and Structural Equation Models

EPSY 908: Structural Equation Modeling II

Area of Concentration

Most students admitted in analytics 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. 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.

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.

Program faculty

Debabrata Dey
  • Davis Area Director, Analytics, Information and Operations Management
  • Ronald G. Harper Professor of Artificial Intelligence and Information Systems
  • Analytics, Information and Operations Management academic area

Analytics and operations doctoral students


Contact Charly Edmonds, doctoral program director.