National University

What you'll study

The program requires 60 credits (20 courses) and explores the entire data science life cycle through both applied and theoretical lenses. Course topics and module emphases described in the catalog include:

• Foundations of doctoral-level data science: nature and methods of the field, lifecycle design, problem definition and management of data for research or industry contexts.
• Descriptive statistics and exploratory data analysis: measures of central tendency, variability, frequency, clustering, and selecting appropriate univariate analyses prior to confirmatory modelling.
• Inferential statistics: sampling distributions, normal distribution, hypothesis testing, power, Type I/II errors, bootstrapping, diagnostic tools and validation techniques for applied research.
• Advanced predictive modelling and machine learning: regression, decision trees, support vector machines, ensemble methods (random forests, gradient boosting), clustering, time series analysis, model evaluation and communicating results.
• Database management and data warehousing: advanced concepts for constructing, assessing and transforming data repositories to support business intelligence and prevent data corruption.
• Big data systems and architectures: relational and distributed storage, distributed computing methods, analytics algorithms, architectural techniques for large datasets, plus ethical considerations and system design to produce insights.
• Data mining and curation: pattern discovery, anomaly detection, association analysis, classification, prediction, similarity assessment, and exploratory techniques to surface hidden trends.
• Dissertation sequence: extended dissertation courses (12-week format) culminating in a manuscript and oral defense; additional credit hours may be added if needed for dissertation completion in accordance with university policies.

Entry requirements

Program requirements published by the university include a minimum GPA of 3.0 (letter grade of B) or higher. Degree conferral requires university approval of the dissertation manuscript and successful oral defense, submission of the approved final dissertation manuscript (original unbound and an electronic copy) to the Registrar, and official transcripts on file for any accepted transfer credit hours. All financial obligations to the university must be settled before issuance of diploma or posted transcript. The program advertises a $0 application fee and no essays or entrance exams for application.

Career prospects

The program prepares graduates to lead original research and to drive data-driven innovation in both academic and industry environments. Emphases on machine learning, predictive analytics, data strategy and responsible AI aim to equip graduates for senior technical leadership, research scientist roles, or faculty positions where advanced methodological knowledge and the ability to translate research into applied solutions are required.

Scholarships & funding angle

The course catalog does not list specific scholarships. Prospective PhD students should consult National University’s financial aid office and the program admissions team for information on scholarships, assistantships, grants, or employer tuition benefits that may apply to doctoral study; ensure any funding arrangements align with dissertation timelines and university enrollment policies.