Marian University

What you'll study

This programme builds a strong foundation in pure and applied mathematics while emphasising computational methods and scientific computing. Core study typically includes a calculus sequence, linear algebra, ordinary differential equations and multivariable analysis, together with courses in discrete mathematics and mathematical proof.

• Numerical Analysis and Scientific Computing – numerical methods for solving equations, interpolation, numerical linear algebra and error analysis.
• Mathematical Modelling – formulation and analysis of models from physics, biology and engineering, dimensional analysis and model validation.
• Probability and Statistics – foundations of probability, statistical inference and applied data analysis.
• Algorithms and Data Structures – computational thinking, algorithm design and complexity relevant to mathematical computing.
• Computational Linear Algebra – matrix computations at scale and applications in scientific computing and data science.
• Electives and Interdisciplinary Options – topics such as optimisation, partial differential equations, cryptography, machine learning, or courses from computer science and engineering departments.
• Capstone Project or Research – a culminating computational project or guided undergraduate research experience applying mathematical and programming tools to an applied problem.

Students gain substantial hands-on experience with programming languages and tools commonly used in computational mathematics, such as Python, MATLAB, R and version control practices, along with lab and project-based courses that emphasise reproducible computation and data handling.

Entry requirements

Applicants should demonstrate strong preparation in mathematics. Typical entry routes include completion of secondary education with good grades in mathematics and related sciences. Where applicable, the programme considers A-levels (including Mathematics and/or Further Mathematics), International Baccalaureate higher-level maths, or the equivalent.

• For applicants from the US, a high school diploma with a strong transcript in mathematics (calculus recommended) and supporting coursework in science or computer science is expected.
• International applicants should present equivalent qualifications and evidence of readiness for university-level mathematics.
• Prior experience with programming is advantageous but not always required; introductory bridging courses are often available for students who need to build coding skills.

Admissions also consider letters of recommendation, a personal statement that describes mathematical interests and goals, and any relevant extracurricular or project experience. Specific course placements may be determined by departmental placement tests.

Career prospects

Graduates with a computational mathematics degree have a wide range of career paths. The combination of mathematical rigour and programming expertise is in demand across sectors.

• Data analysis and data science roles in business, healthcare and technology
• Software development and algorithm engineering, particularly roles that require numerical or scientific computing
• Quantitative roles in finance, risk analysis and actuarial work
• Engineering and applied modelling positions in industry and government
• Operations research, optimisation and decision-support analysts
• Graduate study and research in mathematics, computational science or related disciplines, leading to academic and R&D careers
• Secondary school mathematics teaching (with appropriate certification pathways)

Why study at Marian University

Marian University offers a close-knit undergraduate learning environment where students work directly with faculty on research and applied projects. Small class sizes and personal advising help students build a tailored programme that combines mathematics with computational practice.

• Emphasis on experiential learning, with opportunities for internships, community-engaged projects and capstone research under faculty supervision.
• Access to computing labs and resources that support numerical work, data analysis and collaborative programming.
• Interdisciplinary collaboration is encouraged, allowing students to combine mathematical study with computer science, physics, biology or business courses.
• Supportive career services and connections to regional employers help students secure internships and entry-level positions relevant to computational mathematics.