Rice University

What you'll study

The curriculum blends core mathematics with computational and applied topics. You will take foundational courses in calculus, linear algebra, differential equations and real analysis, alongside computationally focused courses such as numerical analysis, scientific computing, algorithms and data structures, and mathematical modelling. Electives allow depth in areas such as probability and statistics, optimization, partial differential equations, machine learning, and high-performance computing.

Typical year-by-year structure includes introductory calculus and programming in the first year; core mathematics (proof-based courses, linear algebra, and differential equations) and intermediate computation courses in the second year; advanced electives, laboratories and project courses in the third year; and a capstone experience or honours thesis in the final year that emphasises computational implementation and validation of mathematical models.

• Core topics: calculus, multivariable calculus, linear algebra, real analysis, differential equations.
• Computational subjects: numerical analysis, scientific computing, algorithms, data structures, numerical linear algebra, computational PDEs.
• Applied electives: statistics and probability, optimisation, machine learning, computational biology, scientific visualisation.
• Practical experience: programming projects, data-driven modelling, use of numerical libraries and high-performance computing facilities, and an independent capstone or research project.

Entry requirements

Admission to Rice University is competitive; applicants are evaluated on their overall academic record and preparation for a rigorous STEM programme. Successful candidates typically have a strong background in mathematics and quantitative subjects from secondary school, which may include calculus (through multivariable where available), advanced placement or A-level maths, and a programming or computer science foundation.

Recommended preparation:

• High achievement in mathematics courses (including calculus) and other quantitative subjects.
• Experience with programming (Python, C/C++, Java or similar) or coursework in computer science.
• Evidence of problem-solving ability through mathematics competitions, research projects, or advanced coursework where applicable.
• For international applicants, credentials equivalent to U.S. secondary school requirements and English language proficiency as required by the university.

Career prospects

Graduates with a computational mathematics degree are well placed for a variety of careers that require quantitative modelling and programming skills. Common pathways include:

• Data science and analytics roles in technology, healthcare, energy and finance firms.
• Software engineering and algorithm development, particularly in companies that need numerical and scientific computing expertise.
• Quantitative roles in finance, risk management and actuarial work.
• Research and development positions in industry or government laboratories that work on simulation, modelling and computational methods.
• Postgraduate study in mathematics, computer science, statistics, engineering or related fields leading to research or academic careers.

Why study at Rice University

Rice combines a strong mathematics curriculum with access to computational resources and close faculty mentoring in a residential-college environment. The university emphasises undergraduate research and offers opportunities to work alongside faculty on computational projects, internships with Houston-area industries and interdisciplinary collaborations with engineering, computer science and life sciences faculties.

Students benefit from small class sizes, personalised advising, and access to high-performance computing facilities and research centres. The university’s location in a major metropolitan area provides connections to technology, energy and medical sectors for internships and employment, while the academic environment supports both preparation for industry and for competitive graduate programmes.