Boise State University

What you'll study

The Computational Mathematics track builds a strong foundation in core mathematical principles while emphasising computation, numerical methods and mathematical modelling. You will study a sequence of calculus courses, linear algebra and differential equations, then move into higher-level mathematics such as real analysis and advanced linear algebra to develop theoretical understanding.

• Core mathematics: multivariable calculus, linear algebra, ordinary differential equations, real analysis.
• Computational and applied topics: numerical analysis, numerical linear algebra, scientific computing, numerical methods for differential equations, mathematical modelling and simulation.
• Programming and tools: practical training in high-level languages and environments commonly used for computation (for example Python, MATLAB and other scientific computing tools), data structures and algorithmic thinking as applied to numerical problems.
• Statistics and probability: applied probability and mathematical statistics to support modelling and data analysis.
• Capstone and electives: a senior capstone or project-based course involving computational projects, plus electives that may include optimization, computational geometry, machine learning foundations, or courses from computer science and engineering to tailor the degree to specific career interests.

The degree is offered within a broader undergraduate curriculum, so you will also complete general education requirements and have opportunities for undergraduate research, internships and cross-disciplinary coursework.

Entry requirements

Applicants are expected to meet the university's undergraduate admission standards and demonstrate preparation in mathematics. Typical preparation includes successful completion of precalculus and calculus in high school or equivalent college coursework.

• Academic background: strong performance in secondary-school mathematics; applicants who have completed college-level calculus will be better prepared for the major.
• Standard admissions criteria: admission is based on overall academic record and the university's general undergraduate requirements. Specific programme placement or credit for prior coursework may depend on placement tests or evaluation of transcripts.
• International applicants: must meet the university's international admission criteria, including evidence of English proficiency where required.
• Transfer students: transfer applicants should provide college transcripts; transferred credit for mathematics and computation courses is evaluated according to departmental policy.

Career prospects

Graduates with a computational mathematics degree are well placed for roles that require quantitative thinking and computational skills. Common career paths include:

• Data analyst and data scientist roles in technology, finance and health sectors.
• Software developer or computational engineer positions where mathematical modelling and numerical simulation are central.
• Quantitative analyst or risk modeller roles in finance and insurance.
• Operations research, optimisation and systems-modelling roles in industry and government.
• Further study: graduates often pursue graduate degrees in applied mathematics, computational science, statistics, computer science, engineering or related fields, or professional credentials such as actuarial exams.

Boise's regional tech and engineering employers, combined with university-supported internships and undergraduate research, offer practical pathways from study to employment.

Why study at Boise State University

Boise State offers a balance of rigorous classroom instruction and hands-on computational experience within a supportive undergraduate environment. The mathematics faculty are accessible and active in applied and computational research, enabling undergraduate participation in projects.

• Practical computing resources: access to computing labs and software commonly used in scientific computing and numerical analysis.
• Interdisciplinary opportunities: strong collaboration with computer science, engineering and statistical sciences programs that allow students to customise their training for industry or research.
• Undergraduate research and internships: opportunities to work with faculty on computational projects and to secure internships with local and regional employers.
• Career support: university career services and departmental advising help students prepare for employment or graduate study, including help with project portfolios and technical interviews.

Together, these features prepare graduates to apply mathematical methods and computational tools to real-world problems across a wide range of sectors.