The Ph.D. in Data Science and Analytics 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.
We launched the first formal PhD program in Data Science in 2015. Our program sits at the intersection of computer science, statistics, mathematics, and business. Our students engage in relevant research with faculty from across our eleven colleges. As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community. -Sherry Ni, Director, Ph.D. in Data Science and Analytics
This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.
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Dissertations from 2023
Quantification of Various Types of Biases in Large Language Models, Sudhashree Sayenju
Dissertations from 2022
Appley: Approximate Shapley Values for Model Explainability in Linear Time, Md Shafiul Alam
Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics, Jonathan Boardman
Novel Instance-Level Weighted Loss Function for Imbalanced Learning, Trent Geisler
Debiasing Cyber Incidents – Correcting for Reporting Delays and Under-reporting, Seema Sangari
Dissertations from 2021
Integrated Machine Learning Approaches to Improve Classification performance and Feature Extraction Process for EEG Dataset, Mohammad Masum
A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in Episodes of Care Healthcare Delivery System, Lauren Staples
Dissertations from 2020
A CREDIT ANALYSIS OF THE UNBANKED AND UNDERBANKED: AN ARGUMENT FOR ALTERNATIVE DATA, Edwin Baidoo
Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies, Jessica M. Rudd
A Novel Penalized Log-likelihood Function for Class Imbalance Problem, Lili Zhang
ATTACK AND DEFENSE IN SECURITY ANALYTICS, Yiyun Zhou
Dissertations from 2019
One and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles, Bogdan Gadidov
Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis, Jie Hao
Deep Embedding Kernel, Linh Le
Ordinal HyperPlane Loss, Bob Vanderheyden