The Ph.D. in Analytics and Data Science is an advanced degree with a dual focus of application and research - where students will engage in real world business problems, which will inform and guide their research interests.
To ensure that our Ph.D. students in Analytics and Data Science are exposed to the latest issues and challenges of working across a wide variety of data contexts, individuals will be required to engage with one (or more) of the dozens of organizations which have agreed to sponsor doctorate-level projects for a minimum of three semesters (9 credit hours of engagement + 15 credit hours of dissertation research). These organizations span the continuum of application domains, including health care, banking, retail, government, and consumer finance. Students will also continue to work with the faculty adviser through their final year of project engagement and dissertation research.
The unpublished materials in this collection consists of research conducted by PhD candidates as a means to showcase the important work being done in the program.
Submissions from 2017
A Comparison of Decision Tree with Logistic Regression Model for Prediction of Worst Non-Financial Payment Status in Commercial Credit, Jessica M. Rudd MPH, GStat and Jennifer L. Priestley
Logistic Ensemble Models, Bob Vanderheyden and Jennifer L. Priestley
Binary Classification on Past Due of Service Accounts using Logistic Regression and Decision Tree, Yan Wang and Jennifer L. Priestley
Submissions from 2016
An Analysis of Accuracy using Logistic Regression and Time Series, Edwin Baidoo and Jennifer L. Priestley
A Comparison of Machine Learning Techniques and Logistic Regression Method for the Prediction of Past-Due Amount, Jie Hao and Jennifer L. Priestley
Application of Isotonic Regression in Predicting Business Risk Scores, Linh T. Le and Jennifer L. Priestley