Lewis University

What you'll study

The programme builds a solid foundation in core mathematical areas while emphasising computation, numerical methods and modelling. You will typically study multivariable calculus, linear algebra, differential equations and probability and statistics in the early years. Upper-level modules usually include numerical analysis, scientific computing, mathematical modelling, real analysis and abstract algebra, together with courses in algorithms, data structures and applied programming (commonly using languages such as Python, MATLAB or similar tools).

The curriculum commonly features a mix of theory and application: coursework in numerical linear algebra, optimisation, computational methods for partial differential equations, and stochastic modelling prepares you to implement and analyse algorithms for scientific and engineering problems. Many programmes also include a capstone project or undergraduate research component where you apply computational techniques to a substantive problem, and elective options that allow you to specialise in areas such as data science, machine learning, or finance.

Programme structure

• Core mathematics sequence: calculus, linear algebra, differential equations
• Foundational theory: real analysis and abstract algebra
• Computational core: numerical analysis, scientific computing, algorithms
• Supporting topics: probability & statistics, computational modelling, applied electives
• Practical experience: capstone project, laboratory work, and opportunities for internships or research collaboration

Entry requirements

Applicants should hold a high school/secondary school diploma or equivalent with a strong background in mathematics. Typical preparation includes completion of algebra, geometry, precalculus and, where available, calculus. Admissions consider the overall academic record, letters of recommendation, and a statement of purpose; standardised testing requirements may vary and Lewis University provides information on current testing policies.

For transfer students, college transcripts are required and accepted credits are evaluated against programme requirements. Students without sufficient prior programming experience are usually supported through introductory computing modules or preparatory workshops. International applicants must meet English language proficiency requirements.

Career prospects

Graduates with a computational mathematics specialism are well placed for quantitative and technical roles across multiple sectors. Typical career paths include:

• Data analyst or data scientist roles, applying statistical and computational methods to extract insights from data
• Quantitative analyst or model developer in finance and risk management
• Software developer or computational scientist building numerical algorithms and simulations
• Operations research and optimisation specialist in logistics, manufacturing or consulting
• Actuarial positions (with additional professional exams) and roles in insurance analytics
• Continued academic study at graduate level in applied mathematics, computational science, statistics or engineering

Lewis University emphasises hands-on experience, which can help graduates demonstrate practical skills to employers and prepare for both industry roles and further academic study.

Why study at Lewis University

Lewis University offers a student-centred environment with small class sizes and faculty who are active in teaching and applied research. The mathematics department emphasises close mentoring, opportunities for undergraduate research, and collaborative projects that integrate computation and mathematical modelling.

Students benefit from access to computing facilities and software commonly used in industry and research, as well as partnerships and internship opportunities in the Chicago metropolitan area and beyond. The university’s liberal arts context and professional focus aim to develop strong problem-solving, communication and ethical reasoning skills valued by employers and graduate programmes.