University of Texas

What you'll study

This programme blends core mathematical theory with numerical methods and computational practice. You will take foundational courses in calculus, linear algebra, real analysis and discrete mathematics alongside specialised modules in numerical analysis, scientific computing, mathematical modelling and optimisation.

• Core mathematics: multivariable calculus, linear algebra, ordinary differential equations, real analysis and probability and statistics.
• Computational core: numerical linear algebra, numerical solution of differential equations, approximation theory, scientific computing and high-performance computing concepts.
• Algorithms and computing: data structures and algorithms, computational complexity, numerical algorithms and practical programming for mathematical computation (common languages used include Python, MATLAB, and C/C++).
• Applied topics and electives: mathematical modelling, optimisation and control, stochastic processes, computational statistics, machine learning fundamentals and topics in computational geometry or scientific visualisation.
• Capstone and research: an undergraduate capstone, project course or supervised research project that emphasises computational implementation, model validation and interpretation of results.

Entry requirements

Applicants should demonstrate strong preparation in mathematics and related quantitative subjects. Typical preparation includes advanced high‑school mathematics (calculus where available) and evidence of problem-solving ability.

• A recognised secondary school completion qualification or equivalent.
• Strong performance in mathematics courses; prior exposure to calculus is expected and further coursework in statistics, physics or computer science is advantageous.
• Experience with programming is recommended; introductory courses or self-directed projects in languages such as Python, MATLAB or C/C++ strengthen an application.
• Candidates may be asked to provide academic references and a statement that describes their interest in mathematics and computation.

Career prospects

Graduates are well placed for careers that require strong quantitative reasoning and computational skills. The programme prepares students for roles in technology, finance, engineering and research, as well as for further study at graduate level.

• Data scientist, data analyst or machine learning engineer roles that use statistical modelling and numerical methods.
• Software engineering and development positions, particularly in scientific computing or algorithm design.
• Quantitative analyst or risk modeller in finance, employing numerical methods and optimisation techniques.
• Research assistant or postgraduate study in applied mathematics, computational science, statistics or related fields.
• Roles in engineering, operations research, cryptography, and technical consulting where mathematical modelling and simulation are central.

Why study at University of Texas

The University of Texas offers a mathematics department with strong research activity and close collaboration with computing and engineering units, providing access to interdisciplinary projects and resources. Students benefit from proximity to a thriving technology ecosystem, internship and industry partnership opportunities, high-performance computing facilities and active undergraduate research programmes.

Undergraduates can also take advantage of elective courses across computer science, engineering and statistics, join student-led research groups, and participate in mentoring and career support services that help translate computational mathematics training into practical career outcomes.