University of New Haven

What you'll study

The programme combines theoretical foundations with computational practice. Core topics typically include advanced calculus and real analysis for applications, ordinary and partial differential equations, applied linear algebra, numerical analysis and scientific computing. Students study probabilistic modelling and stochastic processes, optimisation and control, and statistical methods for inference and data analysis. Electives allow specialisation in areas such as machine learning, computational finance, dynamical systems, inverse problems and high-performance computing.

Teaching is delivered through a mix of lectures, problem classes and laboratory sessions using industry-standard software and programming languages. The degree normally requires completion of a research thesis or a substantial capstone project in collaboration with faculty or external partners, demonstrating applied mathematical modelling, algorithm development and computational experimentation.

Typical modules

• Advanced Ordinary and Partial Differential Equations
• Numerical Methods and Scientific Computing
• Applied Linear Algebra and Matrix Computations
• Probability Theory and Stochastic Processes
• Optimisation and Control
• Statistical Methods and Data Analysis
• Computational Modelling and Simulation
• Capstone Project or Thesis in Applied Mathematics

Entry requirements

Applicants are normally expected to hold a bachelor's degree in mathematics, statistics, physics, engineering, computer science or a closely related quantitative discipline with a solid grounding in calculus, linear algebra and basic probability. Admissions decisions consider the undergraduate transcript, a statement of purpose outlining quantitative interests, and a curriculum vitae. Programs commonly request one or two academic or professional references. Some applicants may be asked to demonstrate programming experience in languages such as Python, MATLAB or C/C++.

International applicants must demonstrate English language proficiency in line with the university's requirements. The programme may consider applicants with non-traditional backgrounds if they can demonstrate adequate mathematical preparation through prior coursework or bridging work.

Career prospects

Graduates move into a wide range of quantitative roles in industry, government and academia. Typical job titles include data scientist, quantitative analyst, modelling and simulation engineer, operations research analyst, and applied mathematician in sectors such as finance, insurance, engineering, energy, pharmaceuticals and technology. The programme also provides the foundation for continued study at PhD level for those pursuing careers in research or university teaching.

The strong computational emphasis prepares students for roles that require algorithm development, large-scale simulation, statistical inference and machine learning. Career services at the university support placement through internships, employer events and networking opportunities with regional companies and research organisations.

Why study at University of New Haven

The University of New Haven offers a focused, application-oriented master’s with small class sizes and close faculty supervision, enabling hands-on project work and direct access to computing resources. Located on the Connecticut coast near New Haven and within reasonable distance of major industry hubs, the university provides opportunities for internships and collaboration with engineering, computer science and business programmes.

The institution emphasises experiential learning and interdisciplinary collaboration, letting students tackle real-world problems in partnership with faculty or external partners. Graduates benefit from the university’s career support services and regional industry connections that help translate mathematical training into professional opportunities.