Ph.D. in Analytics, Information and Operations

Program information

Faculty research often is motivated by challenges faced by firms across 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.

Application deadlines

  • Priority: December 15, 2023

  • Final: January 10, 2024



  • 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

    Part of our mission is to develop effective teachers. To that end, all doctoral students are required to teach at least two sections as independent instructors. The school and university prepare and reward doctoral students for excellence in teaching through various programs and awards.

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 930: Educational Psychology

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

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.

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

Ben Sherwood
  • Associate Professor
  • Jack and Shirley Howard Mid-Career Professor
  • Analytics, Information and Operations Management academic area

Analytics, information, and operations doctoral students


Contact Charly Edmonds, doctoral program director.