Northern Arizona University

What you'll study

The Computational Mathematics pathway blends core mathematical theory with numerical methods and scientific computing. You will progress through the standard calculus sequence and courses in linear algebra and differential equations, then move into more specialised topics that emphasise computation and applications.

• Core mathematics: calculus sequence, multivariable calculus, linear algebra, real analysis and abstract algebra foundations that develop rigorous problem-solving and proof skills.
• Computational and applied courses: numerical analysis, scientific computing, numerical linear algebra, computational differential equations and mathematical modelling.
• Programming and tools: instruction in programming languages and environments commonly used in numerical work (for example Python, MATLAB or similar), high-performance computing concepts and software for data handling and visualization.
• Probability and statistics: courses in probability, mathematical statistics and applied statistical methods to underpin data-oriented modelling and inference.
• Capstone and research: a senior capstone project or undergraduate research option that typically requires designing and implementing a computational solution to a mathematical or applied problem; opportunities exist to work with faculty on funded research or present at campus symposia.
• Electives and interdisciplinary options: electives allow you to tailor study toward areas such as computational science, data analytics, finance, operations research or applications in the physical and life sciences; collaboration with computer science, engineering or earth-science courses is common.

Entry requirements

Admission to Northern Arizona University requires a completed secondary school qualification and meeting the university's general admission standards. For the mathematics programme, applicants are expected to have a strong mathematics background from high school, typically including algebra, geometry, trigonometry and, where available, precalculus or calculus.

• Successful applicants normally demonstrate strong grades in maths-related subjects and solid analytical reasoning skills.
• Preparation in programming or computer science is beneficial but not required; many students begin programming during their first year.
• Placement testing or introductory assessment may be used to determine the appropriate starting mathematics course.
• Transfer applicants should have completed transferable college-level mathematics (for example calculus) and submit transcripts for review; articulation agreements and advising help ensure correct placement in the programme.

Career prospects

Graduates with a computational mathematics degree have versatile skills valued across many sectors. Employers seek their combination of mathematical reasoning, numerical methods and programming ability for roles that require modelling, analysis and software-enabled solutions.

• Typical roles: data analyst, quantitative analyst, numerical modeller, software developer for scientific applications, operations research analyst, and systems engineer.
• Industry sectors: technology and software firms, finance and actuarial services, engineering consultancies, energy and environmental modelling, aerospace and defence, and government research labs.
• Further study: many graduates pursue postgraduate study in applied mathematics, computational science, statistics, data science or related engineering disciplines; the degree provides solid preparation for research-based or professionally oriented master's and doctoral programmes.
• Other pathways: with additional teacher preparation, graduates can move into secondary mathematics teaching; internships and campus career services help connect students with regional employers and research internships.

Why study at Northern Arizona University

Northern Arizona University offers a student-centred learning environment with opportunities for close interaction with faculty and hands-on computational experience. The programme emphasises undergraduate research, project-based learning and applied computation, so students gain practical experience alongside theoretical training.

• Undergraduate research and mentorship: NAU encourages students to participate in faculty-led research and present at campus and regional conferences.
• Practical computing resources: access to computing labs, software used in numerical analysis and opportunities to work with larger-scale computing resources for intensive projects.
• Interdisciplinary collaboration: easy collaboration with departments such as computer science, engineering, physics and earth sciences for cross-disciplinary projects and internships.
• Career support: campus career services, internship partnerships and alumni networks help students explore industry placements and prepare for postgraduate study.