The PhD in Business Analytics has five learning objectives aimed at training candidates to perform research consistent with the college’s mission to generate nationally and internationally recognized outcomes that improve the world.

  1. Gain deep knowledge of the literature and developments in a specific area within analytics in order to further the field’s appropriate methodological tools.
  2. Fulfill the growing need for faculty that can teach business analytics at the undergraduate and graduate levels.
  3. Conduct research that addresses real-world problems using quantitative and methodological models to visualize and interpret data.
  4. Develop and test research hypotheses by acquiring data from primary and secondary sources.
  5. Clearly communicate research findings and be able to present work at national and international conferences.


The PhD in Business Analytics requires 48 credit hours of coursework, excluding dissertation hours.

Ph.D. Core – 12 credits

  • BZAN 615 – Statistical Learning
    • Focuses on statistical models for supervised learning problems. The course covers linear methods for regression and classification. Model selection, bias-variance tradeoff, subset selection and shrinkage methods are discussed.
  • BZAN 620 – Applied Optimization and Data Analytics
    • Applied optimization is a core and central stream in data mining, machine learning, operations research, and management science. This course presents modeling techniques in optimization and data analytics, focusing on problem formulations and solutions in applying data-intensive optimization techniques for research and application problems in a sufficiently broad domain.
  • BZAN 643 – Data Science for Business Research
    • Presents recent development in data science techniques and their applications in current business research. Topics include causal inference with machine learning, text as data, and network analysis. The course demonstrates the importance and opportunities of combining new data sources and data analytics methodologies to generate business insights.
  • BZAN 645 – Machine Learning
    • Topics in Machine Learning and Artificial Intelligence that are relevant for solving business problems. This course covers supervised learning methods (such as classification and regression trees and artificial neural networks), unsupervised learning methods (e.g., cluster analysis and dimension reduction), and semi-supervised learning.

The four core courses listed above provide a solid foundation in data and analytics. Beyond that, you are free to specialize in your research domain with your PhD Advisor’s guidance. You have flexibility to customize your doctoral coursework.

Elective Courses

In consultation with their advisor, students customize their remaining doctoral coursework (33 credit hours), often taking courses in other departments including Computer Science, Economics, Industrial Engineering, and Mathematics.