National University

What you'll study

The curriculum is built from seven core courses, specialization courses and a three-course capstone. Core work covers statistical modelling and data analysis using R; regression and error analysis; data forms and model building with ethical application of analytics; data management for analytics including acquisition, auditing, cleansing, feature engineering and visualization; data mining and predictive modeling such as classification and decision trees; methods for continuous data (descriptive statistics, goodness-of-fit, correlation, single and multiple linear regression, ANOVA/ANCOVA); and categorical data methods including contingency tables and generalized linear models. An advanced analytics course explores longitudinal methods, factor and principal components analysis, multivariate logistic regression and multivariate ANOVA. Specializations are offered in Artificial Intelligence and Optimization, Business Analytics, Database Analytics, Health Analytics and AI Leadership. Examples of Business Analytics topics include performance and supply chain metrics, data quality and cleansing, predictive analytics for marketing and enterprise decision support. The capstone sequence (three courses) progresses from project proposal and framing to implementation, technical writing and a final written thesis; students must complete all core and specialization courses before beginning the capstone, and enrollment requires a minimum GPA of 3.0 or Lead Faculty approval.

Entry requirements

The program page notes a $0 application fee and that no essays or entrance exams are required for application. To earn the degree students must complete at least 45 semester credit hours; up to 9 semester credit hours of equivalent graduate work from an accredited institution may be applied if not already used toward another advanced degree. Applicants should refer to the university's graduate admissions requirements for full evaluation details.

Career prospects

The degree prepares graduates for roles that require building and maintaining analytics pipelines and applying machine learning and AI to real-world problems. Training emphasizes statistical modelling, predictive analytics, data engineering and applied AI, supporting career paths in data science, analytics, machine learning engineering and domain-focused analytic roles across business, health and technology sectors.

Scholarships & funding angle

The source does not enumerate scholarships. Prospective students should consult the university's graduate funding pages for available scholarships, grants, employer partnerships and the possibility of applying transfer credit to reduce program load.