Murray State University

What you'll study

The Computational Mathematics track builds a solid foundation in pure and applied mathematics while emphasising numerical methods and computational tools. Core coursework typically includes the calculus sequence, linear algebra, ordinary differential equations and real analysis, together with discrete mathematics and abstract algebra to ensure strong theoretical understanding.

• Numerical analysis and scientific computing — floating point arithmetic, error analysis, root-finding, interpolation, numerical integration and differentiation.
• Numerical linear algebra — direct and iterative solvers, matrix factorizations and eigenproblems as used in large-scale computation.
• Computational methods for differential equations — finite difference and finite element methods for ODEs and PDEs.
• Optimization and applied modelling — constrained and unconstrained optimisation, least squares, modelling techniques for applied problems.
• Probability and mathematical statistics — foundations for data analysis and uncertainty quantification.
• Programming and software skills — practical training in languages and environments commonly used in scientific computing (for example Python, MATLAB, and applicable libraries), data structures and algorithmic thinking.
• Capstone or senior project — an independent, supervised project that typically involves modelling, computation and written and oral presentation of results.

Students may also choose electives in machine learning, scientific visualisation, high-performance computing, numerical PDEs, or domain areas such as computational biology, computational finance or engineering applications. Practical experience is reinforced through laboratory courses, project-based classes and opportunities for undergraduate research with faculty.

Entry requirements

Murray State admits students who have completed a strong high-school programme. Successful applicants to the Bachelor of Science in Mathematics with a computational emphasis normally present:

• High-school diploma or equivalent with strong performance in mathematics (including algebra, geometry and precalculus); completion of calculus or college-level mathematics before entry is advantageous.
• A competitive overall academic record demonstrating readiness for a STEM degree; colleges may consider GPA, course rigor and academic recommendations.
• Evidence of quantitative preparation and problem-solving ability; experience with programming is helpful but not always required.
• For transfer applicants, college-level credit in calculus and linear algebra will typically be reviewed for placement and programme progression.
• International applicants must meet the university's English language proficiency requirements and provide documentation of prior academic credentials.

Specific admissions criteria and any standardised testing policies are set by the university and should be checked with Murray State's admissions office.

Career prospects

Graduates of the computational mathematics programme are prepared for a wide range of careers that value strong quantitative and computational abilities. Typical career paths include:

• Data analyst or data scientist roles in business, healthcare and government
• Quantitative analyst positions in finance and risk management
• Software development and engineering roles that require algorithmic and numerical skills
• Modelling and simulation specialist roles in engineering, energy, environmental science and manufacturing
• Actuarial careers (with additional examination preparation)
• Further study in graduate programmes (mathematics, applied mathematics, statistics, computer science, engineering) and research careers
• Secondary school mathematics teaching (with completion of required certification pathways)

The programme's computational focus and project experience help graduates transition into industry internships and entry-level positions, and provide a strong foundation for postgraduate study.

Why study at Murray State University

Murray State offers a personalised undergraduate experience with small class sizes and close access to faculty in the Department of Mathematics and Statistics. The department emphasises undergraduate research and applied training, making it straightforward for students to participate in projects that use modern computational tools and high-quality laboratory facilities.

• Faculty mentorship: opportunities to work directly with faculty on research and applied projects.
• Practical computing resources: access to computing labs, research software and instruction in languages and libraries used in industry and research.
• Interdisciplinary connections: ease of collaboration with engineering, computer science, physics and business departments for joint projects and electives.
• Career support: internship pathways, career services and regional industry partnerships that help students secure practical experience before graduation.

Overall, the programme is well suited to students seeking a balance of mathematical rigour and hands-on computational training, delivered in a supportive, undergraduate-focused environment.