Seattle University

What you'll study

The programme emphasises practical and computational approaches to mathematics. Core topics typically include advanced ordinary and partial differential equations, numerical analysis, scientific computing, mathematical modelling, and applied linear algebra. Students work with algorithmic techniques for solving large‑scale problems, error analysis, and methods for simulation and optimisation.

Typical modules and subject areas you can expect:

• Advanced Ordinary Differential Equations — theory and applications to dynamical systems.
• Partial Differential Equations — analytical and numerical methods for spatially varying systems.
• Numerical Analysis / Scientific Computing — finite difference and finite element methods, iterative solvers, stability and convergence.
• Mathematical Modelling — translating physical, biological or social systems into mathematical form and analysing model behaviour.
• Applied Linear Algebra and Numerical Linear Algebra — matrix computations, eigenvalue problems and applications in data and simulation.
• Probability and Mathematical Statistics — probability theory with applied inference methods for stochastic modelling.
• Computational Methods and Programming — scientific programming in languages such as MATLAB, Python, or Julia, and high‑performance computing concepts.
• Electives / Special topics — options may include optimisation, inverse problems, computational fluid dynamics, machine learning for scientists, or financial mathematics.
• Capstone Project or Thesis — many students complete a project applying techniques to a real dataset or a thesis involving original analysis under faculty supervision.

The programme is commonly structured to combine coursework with a culminating research or applied project. Small class sizes enable close interaction with faculty and opportunities for collaborative work with other departments, such as computer science, engineering, or data science.

Entry requirements

Applicants should normally hold a bachelor's degree in mathematics, applied mathematics, statistics, physics, engineering, computer science, or a closely related quantitative discipline. The department looks for evidence of strong undergraduate preparation in calculus, linear algebra, differential equations and basic probability/statistics. Prior coursework in numerical methods and programming is highly desirable.

Typical application components:

• Official transcripts documenting a suitable quantitative undergraduate degree;
• A personal statement describing academic background, professional goals and areas of interest;
• Letters of recommendation (usually two or three) from academic or professional referees who can attest to your quantitative skills;
• A current CV or résumé;
• Proof of English language proficiency for applicants whose first language is not English (e.g. TOEFL or IELTS) in line with the university's requirements.

Seattle University assesses applications holistically. While standardised tests such as the GRE may be requested in some cases, they are not always required; consult the department for the current testing policy. Applicants with non‑traditional backgrounds but strong quantitative ability and relevant experience may be considered.

Career prospects

Graduates of an applied mathematics master's programme enter a wide range of careers that value advanced analytical, modelling and computational skills. Common career paths include:

• Data scientist or quantitative analyst in technology, finance, healthcare or consulting firms;
• Computational scientist or numerical analyst working on simulation and modelling in engineering, energy, or environmental sectors;
• Software engineer focusing on scientific computing, algorithms or machine learning;
• Research scientist or analyst in government labs, national agencies or private research centres;
• Further academic study—many graduates proceed to PhD programmes in applied mathematics, computational science or related fields.

Situated in Seattle, students benefit from proximity to tech, aerospace and bioinformatics employers, which can facilitate internships, collaborative projects and industry placements that help transition into applied roles.

Why study at Seattle University

Seattle University offers a smaller, student‑centred learning environment with opportunities for close mentorship from faculty who carry out applied and computational research. The university’s Jesuit educational mission emphasises ethical reflection and social responsibility, which complements technical training for those who want to apply mathematics in societally beneficial ways.

Being located in Seattle gives students ready access to a large regional technology and research ecosystem including software companies, biotech firms and engineering organisations—opening pathways for internships, project collaborations and employment. The department also supports interdisciplinary work across computer science, engineering and the natural sciences, and provides modern computing resources and laboratory facilities for numerical experimentation.

Overall, the programme is a strong fit for students seeking rigorous quantitative training combined with applied, hands‑on experience to prepare for careers in industry, government or further research.