University of San Diego

What you'll study

The programme blends applied mathematics, computer science and domain-specific applications to give students practical skills in modelling, simulation and data-driven analysis. Typical core topics include numerical methods for differential equations, scientific computing, numerical linear algebra, statistics for computational science and algorithms.

Students normally take a mix of core courses and electives, with common elective subjects including machine learning and data analytics, optimisation, high-performance and parallel computing, computational fluid dynamics, bioinformatics or geophysical modelling depending on individual interests.

Instruction emphasises hands-on programming and computational projects. Coursework often requires proficiency in languages and tools such as Python, C/C++, MATLAB, R and parallel programming frameworks (MPI, OpenMP, GPU programming). The programme culminates in a capstone project or thesis where students collaborate with faculty or industry partners to address a real-world computational problem.

Entry requirements

Applicants are expected to hold a bachelor’s degree from an accredited institution, typically in a STEM discipline such as mathematics, physics, engineering, computer science or a closely related field. A strong foundation in calculus, linear algebra, differential equations and at least one programming language is usually required.

Typical application components include:

• Transcripts demonstrating undergraduate preparation in quantitative subjects
• Statement of purpose outlining academic background, research or project experience and goals
• Curriculum vitae or résumé detailing relevant coursework, projects and work experience
• Letters of recommendation from academic or professional referees

Some applicants may be required to demonstrate additional preparation through prerequisite coursework or bridge modules if their undergraduate training lacks certain mathematics or programming components. English language proficiency evidence is required for applicants whose first language is not English.

Career prospects

Graduates of computational science programmes typically move into roles that require strong quantitative and programming skills. Common job titles include computational scientist, data scientist, machine learning engineer, simulation engineer, quantitative analyst and software developer for scientific applications.

Employment sectors include technology companies, biotech and pharmaceutical firms, defence and aerospace contractors, energy and environmental consulting, finance, national laboratories and academic research groups. The practical project and capstone elements of the programme also prepare graduates for further research at the doctoral level.

Why study at University of San Diego

The University of San Diego offers a smaller, interdisciplinary environment that emphasises close faculty interaction and applied, project-based learning. Its location in the San Diego region provides proximity to a strong ecosystem of biotech, defence, technology and research institutions, creating opportunities for internships and industry collaboration.

Students benefit from access to modern computing facilities and research centres on campus, and the curriculum is designed to balance theoretical foundations with practical computing skills. The university’s focus on experiential learning means many students complete substantive capstone projects with local partners or faculty-led research groups, helping build a portfolio of applied work for prospective employers.