Loyola University Chicago

What you'll study

The programme emphasises numerical analysis, algorithm design and the use of high-performance computing to solve applied mathematical problems. Core topics typically include numerical linear algebra, numerical solution of ordinary and partial differential equations, numerical optimisation, and scientific computing. Students also study mathematical modelling and approximation theory, as well as probability and stochastic methods when applied to computational problems.

Teaching is delivered through a combination of lecture courses, computational laboratories and seminars. Many students complete a substantial capstone component: either a supervised computational research project or a practicum that applies numerical techniques to a problem drawn from engineering, finance, data science or the physical sciences. Coursework often requires proficiency in programming (for example Python, MATLAB or C/C++), use of mathematical software, and an introduction to parallel and GPU computing for large-scale simulations.

• Numerical Analysis and Error Analysis
• Scientific Computing and Algorithms
• Numerical Linear Algebra
• Computational Methods for PDEs
• Optimisation and Inverse Problems
• Mathematical Modelling and Simulation
• Data Analysis and Computational Statistics (optional/elective)
• Capstone Project or Thesis in Computational Mathematics

Entry requirements

Applicants should normally hold a bachelor’s degree in mathematics, applied mathematics, computer science, engineering, physics or a closely related quantitative discipline. A solid foundation in calculus, linear algebra, differential equations and basic real analysis is expected. Prior programming experience and familiarity with numerical computing are strongly recommended.

Typical application materials include official transcripts, a personal statement describing your quantitative background and research or technical interests, and letters of recommendation. Where applicants lack specific coursework, the department may recommend or require preparatory undergraduate courses in analysis or numerical methods before full progression in the master’s programme. International applicants must meet the university’s English language requirements.

Career prospects

Graduates with a master’s in Computational Mathematics are well placed for roles that combine mathematical modelling and computing. Common career paths include positions as computational scientists, numerical analysts, quantitative analysts in finance, data scientists, software engineers focused on scientific applications, and simulation or modelling engineers in industrial research.

The programme also provides a strong foundation for students wishing to continue to doctoral study in applied mathematics, computational science or related fields. Loyola’s location in Chicago offers access to regional employers in finance, technology, manufacturing and research laboratories, which can support internships and collaborative projects.

Why study at Loyola University Chicago

Loyola University Chicago offers a department environment with small class sizes and opportunities for close faculty supervision. The university emphasises interdisciplinary collaboration, enabling computational mathematics students to work with researchers in computer science, engineering, physics and business on real-world problems. Faculty research areas commonly intersect numerical analysis, scientific computing and applied modelling, providing pathways to join active research projects.

Students also benefit from Loyola’s urban location, which gives access to a wide range of industry partners, research centres and internship opportunities across the Chicago region. The university provides academic support services, computing resources and career services that help students translate technical skills into professional roles or further academic study.