Benedictine University

What you'll study

This programme combines core quantitative methods with applied management topics. Typical modules cover statistical inference and applied regression, optimisation and linear programming, forecasting and time series analysis, simulation and stochastic models, data mining and machine learning, database management and data visualisation, and decision analysis. Students also study applied topics such as managerial economics, supply chain analytics, project management and risk management to place quantitative methods into a business context.

The curriculum emphasises hands-on tools and programming—students commonly use software and languages such as R, Python, SQL and optimisation solvers—and culminates in a capstone project or practicum in which students apply quantitative methods to a real organisational problem. The programme is structured to accommodate full-time and part-time study, with evening and blended delivery options frequently available to support working professionals.

Entry requirements

• A recognised bachelor’s degree from an accredited institution. Candidates with degrees in business, economics, mathematics, engineering, computer science or related fields are particularly well suited.
• A demonstrated quantitative foundation: coursework in calculus, statistics or introductory programming is normally expected. Applicants without formal prerequisites may be asked to complete bridge modules.
• Transcripts and a current CV or résumé. Some applicants will be asked for a statement of purpose describing their quantitative experience and career objectives.
• Letters of recommendation are commonly requested; one academic or professional reference is typical.
• Standardised tests (such as the GRE) may be optional or considered on a case-by-case basis depending on background and professional experience.
• International applicants must meet English language proficiency requirements as set by Benedictine University.

Career prospects

Graduates leave equipped for roles that require rigorous data analysis and optimisation skills. Common career paths include data analyst, business analyst, operations research analyst, supply chain analyst, forecasting specialist, risk analyst and analytics consultant. Alumni also move into roles in finance (quantitative risk and modelling), healthcare analytics, manufacturing and logistics, and public sector policy analysis. The quantitative grounding also provides a pathway to doctoral study or further specialisation in data science and applied statistics.

Why study at Benedictine University

• Small class sizes and direct access to faculty allow for personalised mentoring and close collaboration on applied projects.
• The programme emphasises ethical decision making and leadership consistent with Benedictine values, preparing graduates to apply quantitative tools responsibly in organisations.
• Benedictine offers flexible scheduling and delivery modes to accommodate working professionals, including evening and hybrid options.
• Students benefit from practical, career-oriented support including connections to regional employers, internship opportunities and career services geared to graduate outcomes.
• Strong emphasis on applied learning—through case studies, software-focused labs and a capstone practicum—ensures graduates can translate quantitative techniques into business impact from day one.