Chapman University

What you'll study

This programme blends rigorous quantitative theory with practical computing and business applications. Core topics typically include mathematical optimisation, stochastic processes, statistical inference, simulation modelling and decision analysis. Coursework emphasises the translation of quantitative results into managerial recommendations.

• Core modules: linear and nonlinear optimisation, stochastic modelling and queues, applied probability and statistics, regression and multivariate methods, simulation and Monte Carlo methods.
• Computing and data skills: programming for analytics (Python/R), database management and SQL, machine learning for prediction and classification, time-series forecasting.
• Applied/business modules: revenue and inventory management, supply chain analytics, project and resource scheduling, risk analysis and decision support systems.
• Capstone/Applied Project: a supervised applied project or practicum with a real dataset or partner organisation, integrating modelling, computation and interpretation of results for decision-makers.
• Electives: options often allow specialisation in areas such as financial engineering, marketing analytics, healthcare operations, and advanced machine learning depending on department offerings.

Teaching typically combines lectures, lab work, case studies and a substantial project. Assessment methods include problem sets, coding assignments, group projects and a final capstone or thesis option.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree from an accredited institution. A quantitative undergraduate background (for example, degrees in mathematics, statistics, engineering, economics, computer science or a related field) is strongly preferred because of the mathematical and programming content.

• Academic transcript demonstrating solid preparation in calculus, linear algebra and introductory statistics or probability.
• A current résumé or CV showing any relevant work, internship or research experience.
• A personal statement outlining motivation for the programme and preferred areas of application.
• References: one or two academic or professional references who can speak to quantitative ability and readiness for graduate study.
• English language proficiency proof for applicants whose first language is not English (accepted tests and minimum scores are set by the university).

Standardised tests such as the GRE or GMAT may be optional or considered on a case-by-case basis; applicants with limited quantitative coursework may be asked to provide additional evidence of readiness or to take bridging courses.

Career prospects

Graduates enter a range of data-centric and decision-focused careers. Typical job titles include data analyst, operations research analyst, business analyst, quantitative analyst, supply chain analyst and management consultant. Employers span sectors such as technology, finance, healthcare, logistics, retail and consulting firms.

Alumni also use the degree as preparation for PhD study in operations research, statistics or related fields, or to move into specialised roles such as machine learning engineer or risk modeller as they gain additional programming and domain experience.

Why study at Chapman University

Chapman offers a campus environment with small class sizes and close faculty interaction, which benefits technically intensive graduate programmes. Students gain access to cross-disciplinary expertise across business, computer science and applied mathematics, and there are opportunities to work on applied projects with regional industry partners in Orange County and the wider Southern California economy.

The university provides career services and networking support tailored to postgraduate students, and the programme’s practical focus on computing and capstone experience helps graduates demonstrate job-ready skills to employers. Facilities include modern computing labs and software resources used in industry analytics work.