Dominican University of California

What you'll study

This master's develops competence in quantitative methods and their application to organisational decision making. Core topics typically include statistical inference and regression, predictive analytics and machine learning basics, optimisation and operations research, decision analysis, and data visualisation. Coursework emphasises hands-on experience with analytical tools and programming languages commonly used in industry, such as R, Python or specialised optimisation software. Students generally complete a mix of required modules and electives, and a culminating project: either a capstone consultancy assignment with an external partner or an applied research thesis that integrates quantitative methods with a management problem.

• Statistical Methods for Management: probability, estimation, hypothesis testing and regression modelling applied to business datasets.
• Predictive Analytics and Machine Learning: supervised learning, model evaluation, and practical implementation for forecasting and classification tasks.
• Operations Research and Optimisation: linear and integer programming, network models, queuing and inventory models for operations improvement.
• Management Decision Analysis: decision trees, risk analysis, and multi-criteria decision-making techniques.
• Data Management and Visualisation: data cleaning, relational data concepts, dashboards and storytelling with data.
• Capstone Project or Thesis: applied project with industry sponsor or faculty-led research that demonstrates integration of quantitative methods and managerial insight.

Programme structure

The programme is normally structured around a set of core modules to build a quantitative foundation, elective modules to allow specialisation (for example in finance, healthcare analytics or supply chain), and a final applied project. Delivery may include evening or hybrid options to accommodate working professionals, small seminar-style classes and applied labs.

Entry requirements

Applicants are expected to hold a recognised undergraduate degree in any discipline, though degrees with quantitative content (for example economics, mathematics, engineering, business or the sciences) are an advantage. Typical application materials include an official transcript, a current CV or résumé, a personal statement outlining academic and professional goals, and at least one academic or professional reference. Where English is not the first language, an approved English proficiency test is required. Some programmes may consider prior coursework in statistics, calculus or introductory programming as evidence of readiness; applicants without this background are often advised to complete preparatory coursework.

Standardised tests (such as the GRE or GMAT) may be optional or considered on a case-by-case basis; consult the programme admissions page for the current policy. Relevant professional experience can strengthen an application, particularly for applicants applying to part-time or professional tracks.

Career prospects

Graduates leave with a blend of managerial understanding and quantitative skills that employers seek across sectors. Common career paths include business or data analyst, operations and supply chain analyst, risk analyst, management consultant, product analyst, and roles in pricing or revenue management. Graduates also move into analytics positions in healthcare organisations, financial services, technology firms and non-profit or government agencies. The applied capstone or practicum often provides direct employer contacts and examples of work to show future employers, while the quantitative skillset supports progression into leadership positions that require data-informed decision making.

Why study at Dominican University of California

Dominican University of California offers a learning environment that emphasises personalised instruction and applied learning, with small classes and close access to faculty. The university's location in the San Francisco Bay Area provides proximity to a wide range of employers in technology, finance, healthcare and consulting, supporting internship and networking opportunities. The university’s liberal arts foundation means students gain communication, ethical reasoning and critical-thinking skills that complement technical training, preparing graduates to translate quantitative results into clear managerial recommendations. Faculty with practical experience, opportunities for applied projects with external partners, and career services focused on graduate outcomes help students bridge the gap from study to professional roles.