Northern Arizona University

What you'll study

The programme emphasises numerical and algorithmic approaches to mathematical problems. Core topics commonly covered include numerical analysis, numerical linear algebra, scientific computing, numerical methods for differential equations, optimisation and computational aspects of probability and statistics. Coursework typically balances theory and practical implementation: students study algorithm design and analysis, error and stability analysis, plus hands-on programming for numerical work using languages and environments such as Python, MATLAB and compiled languages as appropriate.

Students can expect modules and subjects such as:

• Numerical Analysis and Approximation Theory
• Numerical Linear Algebra and Matrix Computations
• Scientific Computing and High‑Performance Computing Techniques
• Numerical Methods for Ordinary and Partial Differential Equations
• Computational Optimisation and Inverse Problems
• Mathematical Modelling and Simulation
• Probability, Statistics and Data Analysis for Computational Problems
• Advanced topics seminars in areas such as computational geometry, machine learning methods for numerical problems, or uncertainty quantification

The department normally offers both thesis and non‑thesis options, allowing students who want research experience to pursue an original project under faculty supervision, while those focused on industry roles may choose an applied project or additional coursework. Students frequently take elective courses from related departments (computer science, engineering, physical sciences) to tailor the degree to their interests.

Entry requirements

Applicants should hold a bachelor’s degree in mathematics, applied mathematics, computer science, engineering or a closely related discipline, with a strong record in calculus, linear algebra and proof-based courses. Prior coursework in numerical methods, differential equations and programming is highly recommended. Admissions typically consider academic transcripts, letters of recommendation, a statement of purpose outlining research or career objectives, and a résumé or CV.

International applicants must demonstrate English language proficiency through an accepted test or approved waiver. Prospective students without a directly related undergraduate degree may be admitted conditionally and asked to complete bridging coursework before full graduate standing is granted. GRE requirements vary; consult the programme for current guidance.

Career prospects

Graduates with a master’s in Computational Mathematics are prepared for a range of technical careers where advanced quantitative and computational skills are required. Common career paths include:

• Computational scientist or numerical analyst in industry or national laboratories
• Data scientist, quantitative analyst or machine learning engineer in finance, technology and consulting
• Software engineer specialising in scientific and engineering applications
• Modeller for engineering, environmental sciences, geosciences and biomedical fields
• Research roles or continuation to a PhD in applied mathematics, computational science or related disciplines

Alumni commonly find employment in sectors such as technology companies, engineering firms, government research agencies, environmental and earth science organisations, and academic or industrial research groups.

Why study at Northern Arizona University

Northern Arizona University offers a supportive department with faculty engaged in applied and computational research. The programme provides opportunities for close mentorship through small classes and faculty‑directed research projects. Students benefit from interdisciplinary collaboration across computer science, engineering and the physical sciences, enabling applied projects that address real-world problems.

NAU’s location and institutional connections create pathways for internships and collaborations with regional research centres and industry partners. The department emphasizes practical computational skills alongside mathematical rigour, preparing graduates to transition directly into demanding technical roles or to continue in research and doctoral programmes.