Saint Louis University

What you'll study

This programme blends core mathematics with numerical methods and computer-based problem solving. Students complete a sequence of calculus courses and core theory in linear algebra, real analysis and differential equations, then move into computational and applied modules that emphasise algorithmic implementation and modelling.

• Core mathematical foundations: Calculus sequence, multivariable calculus, linear algebra, ordinary differential equations, and an introduction to proof and real analysis.
• Computational and applied modules: Numerical analysis, scientific computing, numerical linear algebra, computational methods for differential equations, optimisation, and mathematical modelling.
• Programming and software: Practical training in languages and tools commonly used in computational mathematics (for example Python, MATLAB, and C/C++), data structures and basic algorithm design, and reproducible computing practices.
• Probability and statistics: Probability theory, mathematical statistics and an applied statistics course to support data-driven modelling.
• Electives and special topics: Machine learning, cryptography, computational biology, financial mathematics, parallel computing and high-performance computing electives when available.
• Capstone and research opportunities: A senior capstone project or honours thesis that typically involves a substantial computational component, and opportunities to participate in faculty-led research and internships with local industry, government or research labs.
• Degree structure: The major is studied alongside Saint Louis University’s general education requirements, producing a balance of quantitative depth, communication skills and liberal-arts breadth.

Entry requirements

Saint Louis University seeks applicants with a strong secondary-school record and demonstrated achievement in mathematics. Applicants are expected to have completed preparatory coursework that includes algebra, geometry, precalculus and preferably calculus. Successful candidates typically show strength in problem solving and analytical thinking.

• High-school diploma or recognised equivalent with strong performance in mathematics.
• Completion of precalculus and, where possible, introductory calculus prior to matriculation is strongly recommended.
• For transfer applicants, college-level calculus and algebra courses may be reviewed for placement and credit.
• International applicants should meet the university’s academic credential evaluation and English language proficiency requirements.
• Admission is holistic: academic record, recommendations, personal statement and extracurricular engagement in STEM activities or programming projects can strengthen an application.

Career prospects

Graduates with a computational mathematics degree are well placed for a range of technical and analytical careers. The combination of mathematical rigour and programming experience prepares students for roles that require model development, quantitative analysis and software implementation.

• Data and analytics: Data analyst, data scientist or business intelligence roles in public and private sectors.
• Software and engineering: Software developer, simulation engineer, or roles in scientific and engineering computing.
• Finance and risk: Quantitative analyst, actuarial trainee or risk modeller in banking, insurance and fintech.
• Research and advanced study: Graduate programmes in applied mathematics, computational science, statistics, computer science, or engineering.
• Industry and government: Positions in aerospace, energy, healthcare analytics, bioinformatics and research laboratories that require numerical modelling and computational expertise.

Why study at Saint Louis University

Saint Louis University offers a Jesuit education that emphasises intellectual growth, ethical reasoning and community engagement, delivered in small classes where students get personalised attention from faculty. The Mathematics Department encourages undergraduate participation in research and provides hands-on experience with modern computing tools used in industry and academia.

• Close faculty mentoring and opportunities to undertake independent research or honours projects.
• Access to computing facilities and software licences commonly used for numerical and scientific computing, and support for learning contemporary programming languages.
• Strong connections with the St. Louis professional and research community that can support internships and experiential learning in finance, healthcare, technology and government.
• Interdisciplinary pathways allowing students to combine mathematics with minors or double majors in computer science, physics, engineering or data science.
• Comprehensive academic advising and career services aimed at preparing students for employment or graduate study after graduation.