Saint Louis University

What you'll study

The programme combines core training in mathematical and statistical methods with applied coursework and a substantial applied project. Typical core topics include optimisation and linear programming, stochastic processes, simulation modelling, statistical inference, multivariate analysis and forecasting. Students also study data management and programming for analysis (commonly using languages and tools such as Python, R and SQL), decision analysis and risk modelling, and machine learning techniques for predictive analytics.

Elective options allow specialisation in areas such as supply chain and logistics analytics, healthcare systems modelling, financial risk analysis, operations management and business intelligence. The curriculum normally culminates in a capstone practicum or thesis in which students work on a real-world problem, often in collaboration with industry, healthcare providers or university research centres.

Entry requirements

• Academic background: A recognised bachelor’s degree, typically with substantial quantitative content (examples: mathematics, statistics, engineering, computer science, economics, or a quantitative business degree).
• Transcripts: Official academic transcripts demonstrating strong performance in relevant coursework (calculus, linear algebra, probability and statistics are advantageous).
• Supporting documents: A personal statement outlining objectives and relevant experience, and two or more letters of recommendation from academic or professional referees.
• Standardised tests: Some applicants may be asked to provide GRE or GMAT scores; requirements can vary by applicant background and by programme policies.
• English language proficiency: For applicants whose first language is not English, proof of English proficiency is required (for example, IELTS or TOEFL), unless exempted by institutional policy.
• Work experience: While not always mandatory, relevant professional experience or research exposure can strengthen an application, particularly for applicants without a strictly quantitative undergraduate degree.

Career prospects

Graduates are equipped for analytic and decision-focused roles across private and public sectors. Common career paths include positions as operations research analysts, data scientists/analysts, business analysts, supply chain and logistics analysts, risk analysts, quantitative consultants and healthcare systems analysts. Employers span consulting firms, financial services, healthcare providers and hospital systems, manufacturing and logistics companies, technology firms and government agencies. The programme also prepares students for doctoral study in operations research, statistics or related fields.

Why study at Saint Louis University

• Applied focus: SLU emphasises practical, applied analytics through project-based learning and industry-linked capstones that give students experience solving real problems.
• Jesuit educational values: The university’s Jesuit mission promotes ethical decision making and a commitment to service, which is integrated into teaching and research in management sciences.
• Faculty and resources: Students work with faculty who conduct applied research in optimisation, healthcare analytics and business analytics, and benefit from university research centres and computing facilities.
• Location and connections: Based in St. Louis, the university has links to regional hospitals, manufacturing firms, logistics companies and financial institutions, providing internship and employment opportunities.
• Career support: SLU’s career services and local alumni network assist with placement, internships and professional development tailored to quantitative and analytic careers.