University of Texas

What you'll study

The curriculum builds a strong foundation in core mathematical theory alongside applied and computational techniques. Early years emphasise calculus, linear algebra, multivariable mathematics and mathematical reasoning. Core courses progress to differential equations, real analysis, numerical analysis, probability and mathematical statistics, and optimisation.

• Mathematical foundations: single and multivariable calculus, linear algebra, proof-based calculus/analysis
• Applied core: ordinary and partial differential equations, numerical methods for differential equations, scientific computing
• Probability and statistics: introductory probability, mathematical statistics, stochastic processes
• Computation and modelling: numerical linear algebra, computational modelling, data analysis, programming for mathematicians (Python, MATLAB, or similar)
• Advanced electives: optimisation, computational geometry, dynamical systems, mathematical biology, financial mathematics, machine learning and data science electives
• Capstone and research: a senior project, team-based modelling project or supervised research with faculty that develops practical modelling and communication skills

Students may combine the degree with electives or a minor in fields such as computer science, engineering, economics, physics or finance. The programme emphasises both analytical problem solving and practical computational implementation, with opportunities for project-based coursework, internships and research collaborations with applied science and engineering departments.

Entry requirements

Applicants are expected to demonstrate strong prior achievement in high-school mathematics, typically including calculus or an advanced mathematics course. Successful candidates usually show high overall academic performance, strong quantitative marks and evidence of problem-solving ability.

• Typical academic preparation: high-school calculus and algebra, with additional coursework in physics or computer science recommended
• Application materials: standard undergraduate application, personal statement that outlines mathematical interests and experience, and academic references
• Preferred skills: familiarity with basic programming concepts and experience with mathematical problem solving or competitions is advantageous
• Transfer and international applicants: comparable evidence of post-secondary or international qualifications supporting readiness for rigorous mathematics coursework

Career prospects

Graduates leave prepared for a broad range of career paths where quantitative, modelling and computational skills are valued. The degree is strong preparation for immediate entry into technical roles or for further study at graduate level.

• Industry roles: data analyst, data scientist, quantitative analyst, software developer, modelling or simulation engineer
• Finance and operations: actuarial work, risk analysis, quantitative trading and financial modelling
• Engineering and science: computational modelling, systems analysis, scientific computing positions in industry and national laboratories
• Research and academia: progression to master’s or doctoral programmes in applied mathematics, statistics, computer science, engineering or related fields
• Public sector and consulting: analytics and policy modelling roles in government agencies and consulting firms

Why study at University of Texas

The University of Texas provides access to a large, research-intensive environment with active applied mathematics and computational science groups. Students benefit from faculty who work on interdisciplinary problems, connections with engineering, computer science and finance departments, and specialised research centres focused on computational engineering and data science.

The campus’s location and industry links offer internship and collaborative opportunities with technology, engineering and financial employers, while campus resources — computing facilities, tutoring centres and career services — support student learning and professional development. Students can also engage in undergraduate research, attend seminars by visiting researchers and take advantage of cross-disciplinary projects that apply mathematics to real-world challenges.