PhD in Business Administration: Information and Decision Sciences (IDS) Emphasis

Chicago's Public Research University in the Heart of a World-Class Business City

The UIC PhD in Business Administration with an area of emphasis in Information and Decision Sciences (IDS) offers advanced training in business statistics, machine learning and data mining, operations research and operations management. Research cutting across these disciplines is highly encouraged. Students are prepared to contribute to basic theoretical, advanced computational and/or data-driven research with equal emphasis on rigor and impact on business. Research contributions typically have an impact on operations, marketing, finance and/or accounting business functions in industries such as energy, retail, healthcare and banking.

A PhD in Business Administration with an area of emphasis in IDS equips students with analytical and methodological skills in statistics, large-scale data analysis, predictive analytics, social network analysis, operations and supply chain management, optimization, etc. We take an inter-disciplinary approach to problem solving, and our graduates are trained to take this holistic view. Today’s business challenges call for advanced graduates in both academia and industry.

Meet the Team Heading link

Important Dates Heading link

The application deadline for this program is January 15.

Admission Heading link

Admission is competitive and applicants are considered on an individual basis. The college considers applications for full time degree seeking status for the Fall term only. The deadline to submit the application, fee and required materials is January 15. Please see the admissions section of our catalog for application requirements.

Requirements Heading link

The PhD program requires at least 96 credit hours for students entering the program directly from the baccalaureate and 64 credit hours for those entering from a relevant masters degree.

A minimum of 32 hours of dissertation research is required. The course requirements fall into the three categories of foundational work, research specialization and business specialization

A. Foundational Courses

The student must demonstrate proficiency in basic quantitative methods by previous course work or by taking two courses in statistics, computing, or math, depending on student’s interests. Examples of foundational courses include:

  • Applied Statistical Methods (STAT 381-48)
  • Statistical Models and Methods for Business Analytics (IDS 575)
  • Data structures and algorithms (CS 401/CS 501/IDS 517)
  • Real Analysis (MATH 414)

B. Research Specialization Courses

A six-course depth requirement is required. The specific courses depend on the student’s interests and should be chosen in consultation with the research advisor. However, it is expected that the student gather substantial knowledge in a specific area to execute research. Qualifying exams at the end of third semester will be based on these courses or a subset thereof (see examination requirements section for details).

Students interested in machine learning, data mining, operations research, and operations management may choose from the following courses, but are not limited to them:

  • Deterministic Optimization, Stochastic Optimization, Machine Learning/Data-Mining, Stochastic Processes, Network Analysis, Advanced Predictive Models (IDS 576)
  • Big Data Analytics (IDS 561)
  • Advanced Data Management (IDS 521)
  • IDS Seminar (IDS 595)

Students interested in business statistics should choose the following three courses:

  • Introduction to Probability (STAT401)
  • Statistical Theory (STAT 411)
  • Econometrics (ECON, 534)

In addition, three of the following five courses must be taken:

  • Econometrics II (ECON 535)
  • Bus Research & Forecasting I (ID S582/ECON 537)
  • Bus Research & Forecasting II (IDS 583/ECON 538)
  • Research Methodology I (IDS 577)
  • Research Methodology II (IDS 578)

C. Business Specialization Courses

Students are required to take four business courses outside of their research specialization. Courses can be in topics such as operations management, accounting, marketing and finance. Three of these courses must belong to the same business area. In addition to broadening each student’s business exposure, these courses are especially useful for PhD students seeking academic appointments upon graduation. Some of these course requirements may be waived based on a student’s prior graduate training (e.g., MS or MBA).

Research Requirements
Students in machine learning, data mining, operations research and operations management are expected to execute two summer research papers in the first and second summers of the PhD. Each summer paper will be advised by one or more faculty members. The performance on the first summer paper will be taken into account as part of the qualifying examination. Subsequently, students embark on their dissertation research, which can include the summer research papers.

Research and Examination Requirements
Students must pass a qualifying exam, preliminary exam (dissertation proposal), and a dissertation defense.

Qualifying Exam
For students interested in operations research, machine learning and operations management, the qualifying examination is given at the end of the third semester of study and is based on foundational and specialized courses related to the area of the student’s interest and the first summer research paper.

For students specifically interested in business statistics, the qualifying exam is given at the end of the first year and is based on on STAT 401-411 and ECON 534.

Preliminary Exam
The Preliminary Examination consists of the dissertation proposal examination. The preliminary committee includes at least five faculty members, not all five being from the same department.

For a full list of program requirements, visit the UIC Catalog.

Placements Heading link

  • Parshan Pakiman, Postdoc, University of Chicago
  • Eren Ozbay, Discover Financial Services
  • Mehrnaz Amjadi, Senior Data Scientist, NVIDIA
  • Danial Mohseni Taheri, Senior Data Scientist, Walmart Labs
  • Cheng Chen, University of Wisconsin at Milwaukee
  • Dachuan Chen, Nankai University
  • Xiaowei Gong, IBM DemandTec, Foster City, CA
  • Inna Khagleeva, State Street Bank (Boston) Risk-Model Evaluation Group
  • Wendy Yu, BMO Harris Bank, Chicago
  • Jing Cai, BlackRock, Hong Kong
  • Yu Chen, BNY ConvergEx, Iselin, NJ
  • Ziqian Huang, Catalina Marketing, Riverwoods, IL
  • Nordia Thomas, Univ of Wisconsin – LaCrosse
  • Cindy Kao, IBM DemandTec, Foster City, CA
  • Jian Su, KPMG Market Risk Group, New York, NY
  • Jin Zhang, Discover Financial Services, Riverwoods, IL
  • John Sparks, UIC, Department of IDS
  • Yuliya Yurova, Nova Southeastern University, Ft. Lauderdale, FL
  • Yanli Cui, John Deere (Financial Portfolio Management Division), Peoria, IL
  • Zhizin Kang, University of North Carolina – Pembroke

Airong Cai, IBM DemandTec, Foster City, CA

  • Chen Chen, PNC, LaSalle St., Chicago
  • Jun Liu, Georgia Southern University, Statesboro, GA