University of San Diego

What you'll study

The programme emphasises mathematical modelling, analysis and computation. Core topics typically include advanced ordinary and partial differential equations, numerical analysis, scientific computing, and applied linear algebra. Elective modules allow specialisation in areas such as optimisation, stochastic processes and probability, data analysis and machine learning, mathematical biology, and high-performance computing.

Students choose between a thesis or project option in many cases, enabling either a research-focused route with an original investigation under faculty supervision or a practical applied project often carried out in collaboration with an external partner. Course work is complemented by seminars and opportunities to work with faculty on ongoing research problems.

• Advanced Ordinary Differential Equations and Dynamical Systems
• Partial Differential Equations and Boundary Value Problems
• Numerical Methods for Differential Equations
• Scientific Computing and Programming for Applied Mathematics
• Applied Probability and Stochastic Modelling
• Optimisation and Operations Research
• Data Analysis, Statistics and Introduction to Machine Learning
• Modelling in Biology, Engineering or Finance (electives)

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or another closely related quantitative discipline. Strong preparation in calculus, linear algebra, multivariable calculus, differential equations and basic probability/statistics is required.

Typical application materials include official academic transcripts, a statement of purpose outlining research and career goals, letters of recommendation, and a résumé or CV. International applicants must demonstrate English language proficiency according to the University of San Diego’s standard requirements. The programme may assess applicants on a case-by-case basis where undergraduate preparation is non‑standard; bridge coursework could be recommended.

Career prospects

Graduates leave with skills that are in demand across multiple sectors. Career paths commonly pursued include quantitative analyst, data scientist, computational scientist, operations research analyst, risk analyst, and modelling engineer. Employers can be found in technology firms, finance and insurance companies, biotechnology and pharmaceutical firms, defence and aerospace contractors, and consulting companies.

Many students also use the master’s as preparation for doctoral study in applied mathematics, computational science or related disciplines. The programme’s applied focus and ties to local industry help facilitate internships and collaborative projects that build practical experience and professional networks.

Why study at University of San Diego

The University of San Diego offers a supportive learning environment with relatively small classes that enable close interaction with faculty. The mathematics department has active research in applied and computational mathematics, giving students access to advisors working on problems in modelling, numerical methods and interdisciplinary applications.

San Diego’s regional economy, with strengths in biotechnology, defence, software, and data-driven industries, provides opportunities for internships, collaborative projects and employment. Students benefit from campus resources such as computing facilities, career services and interdisciplinary collaboration with engineering, computer science and business programmes.

Overall, the programme combines rigorous mathematical training with hands-on computational experience to prepare graduates for technical roles in industry or further academic study.