Fairleigh Dickinson University

What you'll study

This programme blends a solid foundation in pure and applied mathematics with courses emphasising numerical methods, algorithmic thinking and scientific computing. Typical core topics include calculus, linear algebra, differential equations, real analysis and probability and statistics. Computational‑focused modules cover numerical analysis, scientific computing, mathematical modelling, optimisation, numerical linear algebra and algorithms for large‑scale computation.

• Introductory and advanced calculus
• Linear Algebra and Matrix Computations
• Differential Equations and Dynamical Systems
• Real Analysis and Mathematical Proof
• Numerical Analysis and Error Estimation
• Scientific Computing with languages and tools such as Python, MATLAB or similar
• Data Structures and Algorithms; computational complexity
• Probability, Statistics and Stochastic Modelling
• Optimisation and Operations Research
• Capstone project or senior thesis involving applied modelling, simulation or computational experiment

Students typically take progressively specialised electives in areas such as computational physics, machine learning, data science, cryptography or financial mathematics. Practical experience is gained through laboratory classes, team projects and a capstone that emphasises modelling, simulation and reproducible computational workflows.

Entry requirements

Applicants are expected to hold a recognised secondary school qualification with a strong emphasis on mathematics. Successful candidates normally present prior study in calculus or advanced mathematics; coursework or examinations demonstrating quantitative ability strengthen an application. Colleges and transfer students should show completion of collegiate mathematics courses where applicable.

International applicants must demonstrate equivalent academic preparation and proficiency in English; standardised test scores (when provided) and preparation in STEM subjects are considered. Admissions also review supporting materials such as personal statements and references that speak to quantitative aptitude, problem‑solving experience and programming or technical exposure.

Career prospects

Graduates with a computational mathematics degree are well prepared for careers that require rigorous quantitative reasoning and computing skills. Typical roles include data analyst, quantitative analyst, software developer, numerical modeller, operations research analyst and computational scientist. The combination of mathematics and programming also provides a strong foundation for roles in finance, engineering, technology firms and government research labs.

Many alumni pursue graduate study in applied mathematics, statistics, computer science, data science or engineering, or professional degrees where advanced quantitative training is valued.

Why study at Fairleigh Dickinson University

Fairleigh Dickinson offers a personalised learning environment with relatively small class sizes, allowing close faculty mentoring and opportunities for undergraduate research. The programme emphasises hands‑on computing and applied projects, supported by campus computing resources and laboratory instruction.

Students benefit from interdisciplinary collaboration across science, engineering and business programmes, practical internship and cooperative education opportunities in the surrounding metropolitan region, and career services that help connect graduates with employers in technology, finance and applied research sectors.