Tarleton State University

What you'll study

The programme emphasises numerical analysis and computational techniques used to solve real‑world mathematical problems. Core topics typically include advanced linear algebra, numerical methods for differential equations, scientific computing, mathematical modelling, and optimisation. Students also study applied probability and statistics, numerical linear algebra, and computational aspects of partial differential equations.

Coursework commonly involves hands‑on use of languages and tools such as Python, MATLAB, and high‑performance computing environments to implement algorithms and validate models. The degree usually offers both a thesis and a non‑thesis option: the thesis route focuses on an original research project under faculty supervision, while the non‑thesis route emphasises additional coursework and a project or comprehensive examination.

• Advanced Calculus / Real Analysis
• Numerical Analysis and Algorithms
• Computational Linear Algebra
• Numerical Solution of Ordinary and Partial Differential Equations
• Mathematical Modelling and Simulation
• Optimisation and Scientific Computing
• Applied Probability and Computational Statistics
• Electives in areas such as machine learning, data analysis, or applied mathematics

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science, or a closely related discipline. A strong undergraduate record demonstrating competence in calculus, linear algebra, differential equations and proofs is required. Departments typically look for evidence of quantitative preparation and mathematical maturity.

Specific admissions components may include official transcripts, letters of recommendation, a statement of purpose outlining research or career goals, and a curriculum vitae. International applicants must demonstrate English proficiency through recognised tests or exemptions accepted by the university. Prospective students with deficiencies in prerequisite coursework may be admitted conditionally and asked to complete specified undergraduate courses.

Career prospects

Graduates with a computational mathematics master’s develop skills valued across industry and academia. Common career paths include roles as data scientists and analysts, numerical modelling engineers, software developers for scientific computing, quantitative analysts in finance, and research support scientists in government laboratories or national labs. The programme also prepares students for doctoral study in mathematics, applied mathematics, computational science or related fields.

Employers range from technology companies and engineering firms to finance institutions, energy and environmental consultancies, and research universities. Graduates frequently combine mathematical expertise with programming and computational experience to address problems in simulation, optimisation, uncertainty quantification and large‑scale data analysis.

Why study at Tarleton State University

Tarleton State University offers a focused, supportive environment with accessible faculty who supervise computational and applied research projects. As part of a larger state university system, students benefit from collaborative connections, research facilities and computing resources suitable for algorithm development and numerical experiments.

The programme’s mix of theoretical and applied coursework, together with opportunities for assistantships and hands‑on projects, helps students build both the analytical foundations and practical skills employers seek. Tarleton’s location and industry links in Texas also provide networking and internship possibilities in sectors that use computational mathematics extensively.

Prospective students should consult the Department of Mathematics and Statistics for specific programme paths, faculty research interests, and available graduate support such as teaching or research assistantships.