University of Northern Colorado

What you'll study

The Computational Mathematics pathway combines core graduate-level mathematics with hands-on computational courses. Typical topics include numerical analysis, numerical linear algebra, numerical solution of ordinary and partial differential equations, optimisation and scientific computing. Students also study advanced topics from applied mathematics such as mathematical modelling, approximation theory, and computational probability and statistics.

Programme structure commonly offers a mix of required and elective modules, together with a culminating research thesis or a non-thesis capstone project. Examples of course-level content you can expect are:

• Numerical Analysis and Error Theory
• Numerical Linear Algebra and Matrix Computations
• Computational Methods for Differential Equations (ODEs and PDEs)
• Scientific Computing, including algorithm implementation and performance considerations
• Optimisation and Computational Optimisation Techniques
• Mathematical Modelling and Simulation
• Graduate Seminar in Mathematics — presentation and communication of research
• Electives such as probability and statistics, machine learning foundations, or advanced topics in applied analysis

Students gain practical programming experience (commonly in languages and environments such as Python, MATLAB and C/C++), use of scientific libraries, and training in high-performance computing concepts. Supervised research with faculty leads to the thesis option, while the non-thesis route emphasises a substantial applied project or coursework-based depth.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in mathematics or a closely related discipline (such as applied mathematics, statistics, physics, engineering or computer science) with a strong mathematical background. Admissions typically consider the following:

• Official academic transcripts showing adequate preparation in calculus, linear algebra and differential equations
• A statement of purpose explaining research interests and preparation for computational work
• Letters of recommendation from academic or professional referees who can attest to mathematical ability
• Evidence of programming experience or coursework in numerical methods is advantageous

International applicants must meet the university’s English language proficiency requirements. Some applicants with substantial quantitative experience but without a traditional mathematics degree may be admitted conditionally and required to complete prerequisite coursework. Specific GPA expectations, documentation and any standardised testing policy are provided on the department’s admissions webpage.

Career prospects

Graduates with a master’s in Computational Mathematics are well placed for roles that require strong quantitative and computational skills. Typical career paths include:

• Data scientist or data analyst in industry and public sector organisations
• Quantitative analyst, model developer or risk analyst in finance and insurance
• Computational scientist, simulation engineer or software developer for engineering and technology firms
• Research positions in government laboratories, national research centres or industrial research and development
• Continued academic study at the doctoral level or teaching positions at the college level

The programme’s emphasis on applied computation and modelling also prepares graduates for interdisciplinary collaboration with fields such as environmental science, bioinformatics, and systems engineering.

Why study at University of Northern Colorado

The University of Northern Colorado provides a supportive environment with small class sizes and accessible faculty mentoring in the Department of Mathematical Sciences. Students benefit from hands-on computational training, access to departmental and campus computing resources, and opportunities to work on applied projects with faculty across related disciplines such as computer science, physics and engineering.

Graduate students can pursue research-led thesis work or practice-oriented capstone projects, and the department typically offers teaching and research assistantships that provide professional experience and financial support. The programme’s balance of theory and practical computing prepares graduates to move directly into technical roles or to continue into doctoral programmes.