University of San Diego

What you'll study

The programme blends core graduate-level mathematics with specialised coursework in numerical and computational methods. Students develop a strong foundation in analysis and algebra alongside applied topics and hands-on computational experience.

• Core topics: real and complex analysis, advanced linear algebra, and mathematical modelling that underpin computational approaches.
• Computational modules: numerical analysis, scientific computing, computational linear algebra, numerical methods for partial differential equations, and optimisation algorithms.
• Data and algorithms: numerical probability and statistics, machine learning for mathematical modelling, and algorithm design and analysis.
• Computing skills: high-performance computing, parallel algorithms, software development for scientific applications, and use of languages and tools such as Python, MATLAB, C/C++ and relevant libraries.
• Project and research work: a substantial capstone project or thesis involving original computational work, implementation of algorithms, simulation studies or applied modelling in collaboration with faculty or external partners.

Course structure typically mixes taught modules with a research or applied project in the final year or final semester. Electives allow students to tailor the degree toward areas such as scientific computing, data science, mathematical finance or engineering applications.

Entry requirements

Applicants are normally expected to hold a bachelor's degree in mathematics, applied mathematics, engineering, physics, computer science or another quantitatively rigorous discipline. Typical preparation includes undergraduate courses in calculus, linear algebra, differential equations, and basic programming. Successful candidates usually demonstrate:

• A strong academic record in quantitative subjects (transcripts required).
• Evidence of mathematical maturity such as advanced undergraduate coursework or independent study.
• Programming experience or coursework in computational methods; familiarity with Python, MATLAB or similar is advantageous.
• Supporting materials including a personal statement outlining objectives, and letters of recommendation from academic or professional referees.

Standard additional requirements may include a CV/resume and an interview; some applicants may be asked for GRE scores or other assessments depending on departmental admissions practice. English language proficiency proof is required for non-native speakers.

Career prospects

Graduates with a computational mathematics master's are well placed for roles that require strong quantitative and programming skills. Common career paths include:

• Data scientist or data analyst in technology, healthcare, biotech and consulting firms.
• Quantitative analyst or model developer in finance, risk management and insurance.
• Computational scientist or research engineer working on simulation, optimisation and numerical modelling for aerospace, defence, energy and engineering companies.
• Software engineer or developer specialising in scientific and numerical applications.
• Positions in government laboratories, national research centres and applied research institutes.
• Progression to doctoral research (PhD) in applied mathematics, computational science or related fields for those pursuing academic or advanced research careers.

The programme's emphasis on computational implementation and applied projects helps graduates present a portfolio of practical work to prospective employers.

Why study at University of San Diego

University of San Diego offers a personalised learning environment with relatively small class sizes and accessible faculty who are active in teaching and applied research. The department emphasises close faculty–student interaction, enabling supervised research projects and collaboration across disciplines.

Studying in San Diego provides geographical advantages: proximity to a vibrant technology and biotech sector, research laboratories and a range of applied industry partners that can support project work, internships and networking. The university also provides computational resources, lab facilities and career services that support graduate transitions into technical roles or doctoral study.

Finally, the programme is designed to develop both theoretical understanding and practical skills, preparing graduates to tackle computational challenges across industry and academia while benefiting from the university's supportive campus community and professional development resources.