University of Tulsa

What you'll study

This programme blends core mathematics with computational techniques and practical computing skills. Early courses cover the standard calculus sequence, linear algebra and discrete mathematics. Core computational mathematics topics include numerical analysis, scientific computing, mathematical modelling, ordinary and partial differential equations, and optimisation methods. Students also study probability and mathematical statistics, algorithms and data structures, and courses in computational linear algebra and numerical methods for differential equations.

• Calculus I–III and multivariable calculus
• Linear Algebra and Applied Linear Algebra
• Differential Equations (ordinary and partial)
• Numerical Analysis and Scientific Computing
• Mathematical Modelling and Simulation
• Probability, Statistics and Statistical Computing
• Algorithms, Data Structures and Programming (commonly taught in Python, MATLAB and C/C++)
• Optimisation and Computational Linear Algebra
• Capstone or honours project involving computational research or an industry-sponsored project

Elective options let students tailor the degree toward applications in data science, engineering, physics, finance or computer science. Many students take advantage of interdisciplinary courses offered through the Department of Computer Science and the School of Engineering to deepen practical programming, machine learning or simulation skills. Undergraduate research and a supervised capstone project are common programme components, emphasising reproducible computation and applied problem solving.

Entry requirements

Admission is to the University of Tulsa and then to the mathematics programme; applicants should have a strong background in high-school mathematics (including algebra and precalculus; calculus is highly recommended). Typical academic preparation includes coursework in calculus and a science subject such as physics. The university considers an applicant's overall academic record, letters of recommendation, personal statement and any relevant test scores if submitted.

• Recommended preparation: high-school calculus (or AP/IB equivalents), strong grades in mathematics and a laboratory science.
• Programming experience is advantageous but not required; introductory university programming courses are available.
• Advanced standing: students with AP, IB or other recognised examination credit may place out of introductory courses, subject to departmental rules.
• International applicants: proof of English language proficiency is required according to university policy (e.g. recognised tests or exemptions where applicable).

Career prospects

Graduates with a computational mathematics degree are well placed for roles that require quantitative reasoning, numerical methods and programming. Typical entry-level job titles include:

• Data analyst or data scientist (with additional applied experience or coursework)
• Software engineer or scientific programmer
• Quantitative analyst or modelling specialist in finance and energy sectors
• Computational scientist in engineering, physics or environmental modelling
• Actuarial analyst (with professional exam progression)
• Operations research analyst or optimisation consultant

Graduates also frequently continue to postgraduate study in applied mathematics, computational science, data science, engineering or statistics. The programme’s emphasis on computational skills and a capstone project provides practical experience valued by employers across technology, energy, aerospace, finance and research organisations.

Why study at University of Tulsa

The University of Tulsa offers small class sizes and close faculty mentorship, which benefit students pursuing mathematically intensive and computational work. The Department of Mathematics maintains active ties with the computer science and engineering programmes, enabling interdisciplinary study and collaborative projects. Students have access to modern computing facilities and software used in numerical analysis and scientific computing, and there are opportunities for undergraduate research supervised by faculty.

• Strong emphasis on undergraduate research and guided capstone projects
• Interdisciplinary options with computer science, engineering and physical sciences
• Support for internships and industry partnerships in the Tulsa region and beyond
• Personalised advising and a curriculum that prepares students for both careers and graduate study

These features make the University of Tulsa a suitable place to develop both the theoretical foundations and practical computational skills needed for modern quantitative careers.