Tennessee Technological University

What you'll study

The Computational Mathematics concentration builds a foundation in pure and applied mathematics alongside courses in numerical methods and scientific computing. You will study core mathematical topics, develop programming skills used in numerical analysis, and complete a capstone or research project that applies computational tools to real problems.

Typical modules and subjects

• Calculus sequence – single-variable and multivariable calculus providing the fundamental tools for analysis and modelling.
• Linear Algebra – vector spaces, matrices, eigenvalues and applications to computational problems.
• Differential Equations – ordinary and introductory partial differential equations with applied techniques.
• Real Analysis – rigorous study of limits, continuity and convergence, supporting advanced mathematical reasoning.
• Numerical Analysis/Scientific Computing – numerical methods for root finding, interpolation, numerical linear algebra and numerical solutions of differential equations.
• Algorithms and Data Structures – computational thinking and algorithmic foundations relevant to numerical software.
• Probability and Mathematical Statistics – probabilistic models, statistical inference and their computational implementation.
• Optimization and Numerical Optimization – theory and algorithms for constrained and unconstrained optimisation problems.
• Mathematical Modelling and Simulation – translating applied problems into mathematical form and simulating solutions using software such as MATLAB, Python or similar tools.
• Capstone/Undergraduate Research – a culminating project or research experience in computational mathematics, often undertaken in collaboration with faculty or industry partners.

Students also complete the university general education requirements and may take elective courses in computer science, engineering, economics or the natural sciences to tailor their degree to specific career interests.

Entry requirements

Applicants should hold a high-school diploma (or equivalent) with a solid preparation in mathematics. Recommended prior study includes algebra, geometry, trigonometry and at least one year of calculus if available. Admissions decisions are based on academic transcripts; standardized test scores (ACT/SAT) are considered where submitted but are not the only criterion. Transfer applicants are evaluated on college coursework and should have completed introductory calculus and college algebra or equivalents.

Successful candidates typically demonstrate strong quantitative skills, problem-solving ability and some programming experience or willingness to learn programming languages commonly used in scientific computing (for example Python or MATLAB). Prospective students with gaps in preparation may be advised to take bridge or preparatory courses in calculus or programming during their first year.

Career prospects

A degree in Computational Mathematics prepares graduates for roles that combine mathematical modelling, computation and data analysis. Common career paths include:

• Data analyst or data scientist in technology, healthcare, or business sectors.
• Computational scientist or numerical analyst developing simulation software for engineering and research.
• Software engineer or developer focused on numerical libraries and performance-sensitive applications.
• Quantitative analyst in finance or risk management, applying mathematical models to market data.
• Operations research analyst or optimisation specialist improving systems and processes.
• Actuarial technician or candidate preparing for actuarial exams (with additional specialised coursework).
• Graduate study in applied mathematics, computational science, statistics or related engineering disciplines.

Undergraduate research experience, internships and collaboration with industry partners enhance employability and provide practical examples for portfolios and graduate applications.

Why study at Tennessee Technological University

Tennessee Technological University offers a mathematics programme with strong links to applied and computational work, benefitting from close collaborations with engineering and computer science departments. Students have opportunities for faculty-mentored undergraduate research, project-based capstones and internships with regional employers.

The department emphasises small-class instruction in upper-level courses, enabling close interaction with faculty and personalised mentoring. Facilities supporting the programme include computing labs and access to scientific software used for numerical simulation and data analysis. Career services and industry partnerships in the region support placement and experiential learning, making this degree suitable for students aiming to enter technical roles or to progress to graduate study in computation-intensive fields.