Kent State University

What you'll study

The programme combines rigorous mathematical theory with computational practice. Core topics typically include advanced ordinary and partial differential equations, numerical analysis and scientific computing, mathematical modelling, optimisation, and probability and stochastic processes. Students also study linear and nonlinear dynamical systems, numerical linear algebra, and methods for data analysis and uncertainty quantification.

Instruction mixes lectures, problem-based courses and computing labs; commonly used tools include MATLAB, Python and specialised numerical libraries. Students may take electives drawn from computational science, computational fluid dynamics, inverse problems, finite element methods, mathematical finance, or machine learning, depending on faculty offerings.

The department usually offers both a thesis and a non-thesis (project/coursework) route. The thesis option focuses on an independent research project under faculty supervision and is suitable for those intending to continue to doctoral study. The non-thesis pathway emphasises additional coursework and a capstone project or practicum geared towards professional applications.

Entry requirements

• Bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related field from an accredited institution.
• Evidence of strong preparation in undergraduate mathematics, typically including calculus, linear algebra, differential equations, and an introductory course in real analysis or proofs; coursework in numerical methods or programming is advantageous.
• Official transcripts, a statement of purpose outlining academic and career goals, and academic letters of recommendation.
• International applicants must demonstrate English language proficiency through an approved test unless exempted by institutional policy; additional documentation for international credentials may be required.
• Some applicants—particularly those from non‑mathematics backgrounds—may be admitted conditionally and advised to take specified undergraduate bridge courses before or during graduate study.

Career prospects

Graduates with an MS in Applied Mathematics are prepared for quantitatively demanding roles across a wide range of sectors. Common career paths include data scientist, quantitative analyst, operations researcher, computational scientist, simulation and modelling engineer, and software developer for scientific applications. Employers span finance, technology, engineering firms, manufacturing, energy, healthcare analytics, and government or national laboratories.

The degree also provides a strong foundation for doctoral study in applied mathematics, computational science or related engineering disciplines. Kent State graduates benefit from applied research projects and internship opportunities that help bridge academic training and workplace experience.

Why study at Kent State University

• The Department of Mathematical Sciences offers a balance of theoretical and computational instruction with faculty research expertise in applied analysis, numerical methods, dynamical systems and computational modelling.
• Students have access to university computing resources and collaborative opportunities with nearby engineering and science departments, fostering interdisciplinary projects and applied research experience.
• Kent State’s proximity to regional industry and research centres provides access to internship and employment opportunities in the Cleveland–Akron metropolitan area and beyond.
• Flexible programme structures, including thesis and non‑thesis options, allow students to tailor study toward academic research or professional practice; graduate assistantships and departmental support may be available for qualified candidates.