Roger Williams University

What you'll study

The Computational Mathematics track blends core mathematical principles with numerical analysis, scientific computing and algorithmic design. You will build a foundation in calculus, linear algebra and differential equations, then move into specialised courses that teach how to model, approximate and compute solutions to complex problems.

• Core mathematics: Calculus sequence, Linear Algebra, Ordinary Differential Equations, Real Analysis or Advanced Calculus.
• Computational and numerical methods: Numerical Analysis, Scientific Computing, Computational Linear Algebra, Numerical Solution of PDEs, Approximation Theory.
• Computer science and programming: Programming for Scientists (commonly Python and MATLAB), Data Structures and Algorithms, High‑Performance Computing or Parallel Programming options.
• Applied and modelling courses: Mathematical Modelling, Probability & Statistics, Optimization, Computational Statistics or Machine Learning elective.
• Capstone and experiential learning: Senior Capstone Project or Honors Thesis in computational mathematics, opportunities for undergraduate research with faculty, internships or practicum placements in industry or government labs.
• Electives and interdisciplinary options: Courses drawn from computer science, engineering science, physics or economics to tailor the degree toward data science, computational engineering or finance.

Entry requirements

Admission to Roger Williams University requires a high school diploma or equivalent. For the computational mathematics track applicants should have a strong background in mathematics and evidence of quantitative readiness.

• Recommended preparation: secondary school coursework in algebra, geometry, precalculus and preferably calculus (or equivalent AP/IB college‑level work).
• Programming experience is advantageous: familiarity with at least one language such as Python, MATLAB, Java or C/C++ will help in early computational courses.
• Application materials: completed application, academic transcripts, personal statement, and the names of teachers or counsellors for reference. Some applicants may be asked to submit SAT/ACT scores where required by policy or to take placement assessments in mathematics.
• Transfer applicants: college transcripts and descriptive syllabi for prior mathematics/computer science courses to determine appropriate placement and transfer credit.

Career prospects

Graduates with a computational mathematics degree are prepared for roles that require strong quantitative reasoning and computational skills. Common career paths include:

• Data analyst, data scientist or business intelligence analyst in technology, finance, healthcare or government.
• Software developer or computational engineer working on scientific applications, simulations or numerical modelling.
• Quantitative analyst (quant) roles in finance, risk management and actuarial science after appropriate professional qualifications.
• Research and development positions in engineering, environmental modelling, computational biology and physical sciences.
• Graduate study in applied mathematics, computer science, statistics, engineering or related disciplines leading to advanced research or academic careers.

Why study at Roger Williams University

Roger Williams University emphasizes small class sizes and close faculty mentorship, giving undergraduates regular access to professors for research and project supervision. The university supports hands‑on learning through undergraduate research, internship placements with regional industry and public agencies, and collaborative capstone projects that let students demonstrate applied computational skills.

• Interdisciplinary opportunities: easy cross‑registration with computer science, engineering science and economics to customise your computational focus.
• Facilities and resources: computing labs, software tools commonly used in numerical work (e.g. MATLAB, Python libraries) and access to high‑performance computing resources for larger projects.
• Career support: career services, alumni network in the New England region and relationships with local employers help secure internships and entry‑level positions.
• Preparation for further study: the programme’s balance of theory and computation equips students for competitive graduate programmes in applied mathematics, data science and engineering.