Doctoral DegreeData Sciences Ph.D.

This course provides a more in depth presentation of the theory behind linear statistical models, segmentation models, and production level modeling. Further emphasis is placed on practical application of these methods when applied to massive data sources and appropriate and accurate reporting of results.

This course will introduce the student to advanced research designs relevant to research in data science. The course will show the student how to determine the data required to answer different types of research questions and implement data collection plans. The course will explore experimental and quasi-experimental designs as well as data mining and dataset creation.

This course provides hands-on experience in understanding when and how to utilize the primary multivariate methods Data Reduction techniques, including Principal Components Analysis and Exploratory and Confirmatory Factor Analyses, ANOVA/MANOVA/MANCOVA, Cluster Analysis, Survival Analysis and Decision Trees.

This course follows a research seminar format. The student and faculty develop research proposals, analyses, and reporting in the domain of Generative AI. Topics of special interest in Generative AI are presented by faculty and the student under faculty direction. Topics of special interest vary from semester to semester.

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

This course follows a research seminar format. The student and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning and Predictive AI Modeling. In addition, topics of special interest in Machine Learning and Predictive AI are presented by faculty and the student under faculty direction. Topics of special interest vary from semester to semester.

Topics include the design of advanced algorithms that are scalable to Big Data, high performance computing technologies, supercomputing, grid computing, cloud computing, and Parallel and Distributed Computing, and issues in data warehousing.

This seminar provides support to doctoral students within their specific domains of research. Led by the faculty advisor for that domain, the course is designed to provide a forum where faculty and students can come together to discuss, support, and share the experiences of working in research.

Advancement to candidacy is a prerequisite of this course. This is an individual study course for doctoral students. Content to be determined by the student and the student’s Doctoral Committee. May be repeated for credit.