Loyola University Chicago

What you'll study

The programme builds a strong foundation in pure and applied mathematics while emphasising numerical methods and scientific computing. Core coursework typically includes the calculus sequence, linear algebra, differential equations and an introduction to proofs and real analysis. Computationally focused modules commonly cover numerical analysis, scientific computing, algorithms and data structures, mathematical modelling, and numerical linear algebra.

Students also study probability and statistics, discrete mathematics and applied topics such as optimisation, partial differential equations, and computational methods for data analysis. Practical computing skills are taught using contemporary tools and languages (for example Python, MATLAB and R), and courses often incorporate project work, lab sessions and team-based assignments.

The degree usually culminates in a capstone project or senior seminar where students apply computational techniques to a substantive problem — options include modelling physical systems, large-scale data analysis, simulation studies or an independent research project supervised by a faculty member.

Entry requirements

Admission to Loyola’s undergraduate programmes is based on an overall academic record and preparedness for college-level mathematics. Typical preparation includes a strong high-school curriculum with college preparatory mathematics — algebra, geometry and precalculus, with calculus recommended if available. Successful applicants demonstrate analytical thinking and experience with problem-solving.

Because the programme moves quickly into higher-level mathematics, placement testing or advising may be used to determine the appropriate starting calculus course. Applicants with prior experience in computer science or programming will be well placed for the computational components of the major.

International applicants must meet English language proficiency requirements accepted by the university. Transfer students should consult department advisors about transfer credit for calculus, linear algebra and other foundational courses.

Career prospects

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

• Data scientist / data analyst: applying statistical and computational techniques to extract insights from data.
• Software engineer / developer: building algorithms and software for scientific or commercial applications.
• Quantitative analyst / risk analyst: using mathematical models in finance and insurance.
• Operations research / optimisation specialist: improving decision-making and system performance.
• Actuarial roles: with additional professional exams, graduates can enter actuarial science.
• Graduate study and research: many students progress to Master’s or PhD programmes in mathematics, statistics, computer science or engineering.

Internships and project experience acquired during the degree are valuable for transitioning into industry roles, and computational graduates are in demand across technology companies, financial services, healthcare analytics, government and research institutions.

Why study at Loyola University Chicago

Loyola combines a liberal arts approach with specialised training in mathematics and computing. Students benefit from relatively small class sizes, direct access to faculty, and opportunities for guided undergraduate research and mentored capstone projects. The mathematics department fosters close faculty–student interaction and provides advising to help tailor the curriculum to individual interests.

Located in Chicago, the university offers access to a large and diverse job market, internship opportunities and collaborations with nearby research centres and industry partners. Campus resources such as computing labs, the career centre and academic support services help students develop both technical skills and professional readiness.

The programme’s emphasis on computational methods prepares graduates to tackle applied problems across sectors while maintaining the rigorous mathematical training that supports long-term career flexibility and graduate study.