Murray State University

What you'll study

The programme combines rigorous mathematical foundations with practical computational techniques. Core topics commonly covered include numerical analysis, computational linear algebra, numerical solutions of differential equations, optimisation and scientific computing. Students also study supporting subjects such as real analysis, advanced calculus, probability and statistics, and mathematical modelling.

Instruction emphasises hands-on computation: coursework typically involves programming assignments and projects using languages and tools commonly used in industry and research (for example, Python, MATLAB or Julia), implementation of algorithms, and application of numerical techniques to problems from engineering, physics and data science. Students may choose elective modules in areas such as computational statistics, machine learning, high-performance computing, dynamical systems or partial differential equations to tailor the degree to their interests.

The department generally offers both a thesis and a non-thesis (project or coursework) option. The thesis route is research-focused and suitable for students planning to continue to PhD study, while the non-thesis option emphasises advanced coursework and applied projects aimed at immediate employment in technical fields.

Entry requirements

Applicants should hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a related quantitative field. A solid background in undergraduate analysis, linear algebra and calculus is expected, together with some familiarity with programming and numerical methods. Transcripts demonstrating appropriate preparation are required.

Typical application materials include an academic transcript, a statement of purpose that outlines preparation and goals, and letters of recommendation from academic or professional referees. International applicants must demonstrate English language proficiency through an accepted test or equivalent evidence. Some applicants without a strong mathematics background may be admitted conditionally with a requirement to complete prerequisite coursework.

Career prospects

Graduates with an MS in Computational Mathematics are prepared for roles that require advanced quantitative and computational skills. Common career paths include data scientist or analyst, quantitative analyst in finance, numerical modeller, scientific programmer, software engineer for technical applications, and positions in simulation and optimisation within engineering firms. The degree also serves as preparation for doctoral research in applied mathematics, computational science or related disciplines.

Employers span industries such as technology, finance, energy, manufacturing, government research labs and higher education. Graduates often work on interdisciplinary teams, applying algorithms and simulations to solve complex problems in areas like computational fluid dynamics, predictive analytics, risk modelling and machine learning.

Why study at Murray State University

Murray State offers a supportive, student-centred environment with small classes and close access to faculty, which benefits graduate students undertaking computational research or project work. The Mathematics Department provides opportunities to work on applied projects with faculty and collaborators across disciplines, and the university maintains computing resources appropriate for numerical and data-intensive work.

Graduate assistantships and departmental funding opportunities may be available to qualified students, providing practical teaching or research experience and financial support. The regional setting fosters connections with local industry and research partners, while the programme’s flexibility allows students to focus on either research or applied computational training depending on career objectives.