The Bachelor of Arts/Bachelor of Science in Mathematical Sciences with a Computational Mathematics emphasis at the University of Colorado Boulder combines rigorous mathematical training with numerical methods and programming to prepare students to model and solve real-world problems. It suits students who enjoy abstract reasoning and practical computation, and who want careers in data-driven, engineering or scientific environments.
What you'll study
The computational mathematics pathway builds a foundation in pure and applied mathematics and adds focused training in numerical methods, algorithmic implementation and scientific computing. Students typically complete the core calculus sequence, multivariable calculus, linear algebra and differential equations before moving into upper-division coursework.
- Core mathematics: calculus (single and multivariable), linear algebra, differential equations, discrete mathematics and real analysis fundamentals.
- Computational and applied courses: numerical analysis, scientific computing, numerical linear algebra, optimisation and computational modelling.
- Programming and algorithms: courses that teach high-level and low-level scientific programming (commonly Python, MATLAB and C/C++), data structures and algorithmic thinking.
- Probability and statistics: applied probability, statistical modelling and inference for computational work.
- Electives and interdisciplinary options: students often take electives in computer science, physics, engineering, or geosciences to strengthen domain knowledge for applied projects.
- Capstone/Research: a senior project, practicum or supervised undergraduate research is normally available to integrate mathematical theory with computational implementation on a substantive problem.
Entry requirements
Applicants should demonstrate strong preparation in secondary school mathematics. Successful candidates have completed coursework in calculus and have a background that indicates readiness for rigorous quantitative study. The department values applicants with experience or interest in programming and problem solving.
- For domestic applicants: a competitive high-school academic record with rigorous coursework in mathematics; completion of intro calculus is expected.
- For international applicants: equivalent secondary-school qualifications and proof of English proficiency if required by the university.
- Transfer students: transcripts showing college-level calculus and linear algebra are typically required; course equivalency is reviewed by the department.
- Additional considerations: personal statements, letters of recommendation and evidence of quantitative experience (such as coding or math competitions) can strengthen an application.
Career prospects
Graduates with a computational mathematics degree are well placed for careers that require strong quantitative and computational skills. The programme prepares students for roles across industry, government and academia.
- Data scientist or analyst, applying mathematical modelling and statistics to extract insight from data.
- Software engineer or scientific programmer, developing algorithms and numerical software for simulation or analysis.
- Quantitative analyst in finance, risk management or operations research roles that use optimisation and stochastic modelling.
- Research or technical positions at national laboratories, research institutes and high-tech companies that require numerical simulation and modelling expertise.
- Graduate study in applied mathematics, computational science, computer science, statistics or engineering for students pursuing research or advanced technical careers.
Why study at University of Colorado Boulder
CU Boulder offers an integrated environment for computational mathematics through a mathematics department with active applied and computational research and close ties to computer science, engineering and the physical sciences. Undergraduates have access to research opportunities, faculty mentorship and project-based courses that emphasise numerical implementation and real-data problems.
- Strong collaborative culture with opportunities to work with faculty on computational research projects and to participate in interdisciplinary teams.
- Proximity to national research facilities and a local tech ecosystem provides internship and employment pathways in scientific computing and data-driven fields.
- Resources such as campus computing facilities, specialised software support and student organisations that support coding, modelling and applied mathematics initiatives.
- Options to tailor the degree through electives, minors or combined study with computer science and engineering to match specific career objectives.
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