Marymount University

What you'll study

This master’s programme combines core training in quantitative methods with applied management science topics. Typical core modules cover statistical inference and regression, optimisation and linear programming, simulation and stochastic models, time series and forecasting, and decision analysis. Students also study data management and programming for analytics using tools such as R or Python, as well as coursework in database fundamentals and applied machine learning methods tailored for managerial problems.

Electives allow specialisation in areas such as operations research, supply chain analytics, risk analysis, project management, or advanced predictive analytics. The degree usually culminates in a capstone project or practicum in which students work on a real-world problem for an external client or department—applying modelling, data analysis and decision-support techniques to produce actionable recommendations.

Entry requirements

• Academic qualification: A recognised bachelor’s degree in a related field such as business, economics, engineering, mathematics, statistics, computer science or another quantitative discipline.
• Quantitative preparation: Prior coursework in calculus, introductory statistics and basic linear algebra is normally expected; applicants without this background may be admitted conditionally and required to take preparatory courses.
• Supporting documents: Personal statement outlining your interest and goals, a current résumé or CV, and academic transcripts. Letters of recommendation are commonly requested.
• Standardised tests and English language: Some applicants may be asked to submit GRE scores; international applicants must demonstrate English proficiency through recognised tests unless exempt.
• Professional experience: Relevant work experience is advantageous for applicants seeking the practicum or part-time study options but is not always mandatory.

Career prospects

Graduates enter roles that require strong analytical and decision-support skills. Common job titles include data analyst, business or management analyst, operations analyst, supply chain analyst, risk analyst, and decision-support specialist. The programme also prepares students for roles in consulting, government agencies and non-profit organisations where quantitative modelling and evidence-based policy are needed.

With additional experience or further study, graduates may move into senior analytics positions, operations management, product management or academic and research pathways. The practicum/capstone gives industry-relevant experience that can support transition into internships or full-time roles.

Why study at Marymount University

Marymount University offers a student-centred environment with small class sizes and applied, career-focused teaching. The university’s location near Washington, D.C. provides strong access to government agencies, consulting firms and corporations for internships, practicum projects and employer networking. Faculty typically combine academic expertise with professional experience, emphasising practical problem-solving and use of contemporary analytics tools.

Flexible scheduling options and dedicated career services help students balance study with work and make connections in the regional job market. The programme’s emphasis on applied projects and interdisciplinary collaboration equips graduates to tackle complex, data-driven management challenges across sectors.