Canisius University

What you'll study

The programme focuses on building advanced mathematical tools and computational techniques for modelling, analysis and simulation. Core topics typically include advanced ordinary and partial differential equations, numerical analysis and scientific computing, mathematical modelling, probability and stochastic processes, and optimisation methods.

• Advanced Analysis: rigorous treatment of multivariable calculus, functional analysis concepts relevant to applied problems.
• Differential Equations and Modelling: theory and applications of ODEs and PDEs in physics, biology and engineering contexts.
• Numerical Methods and Scientific Computing: numerical linear algebra, finite difference/finite element methods, error analysis and implementation in high‑level languages.
• Probability, Statistics and Stochastic Methods: foundations of probability, stochastic modelling, and statistical techniques for data arising from mathematical models.
• Optimisation and Operations Research: continuous and discrete optimisation, convex analysis and algorithmic approaches to large‑scale problems.
• Electives and Interdisciplinary Options: students can often choose electives in data science, mathematical finance, computational biology or engineering topics to tailor their studies.
• Capstone Project or Thesis: a substantial project or research thesis applying mathematical and computational methods to a concrete problem, often in collaboration with faculty or local industry partners.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, applied mathematics, statistics, physics, engineering, computer science or a closely related quantitative discipline. Adequate preparation in calculus, linear algebra and introductory differential equations is normally required. Schools may ask for a transcript of academic records, a personal statement outlining objectives and relevant experience, and letters of recommendation.

Where undergraduate preparation is incomplete, applicants may be admitted conditionally and required to take preparatory courses. Familiarity with a programming language (such as Python, MATLAB or C++) and basic numerical methods is strongly recommended for success in the programme.

Career prospects

Graduates with a master’s in applied mathematics pursue a broad range of careers across sectors that rely on quantitative analysis and modelling. Typical roles include:

• Data scientist or data analyst in technology, healthcare, finance and government
• Quantitative analyst or risk modeller in banking and insurance
• Operations research analyst and optimisation specialist for logistics and manufacturing
• Computational scientist or numerical modeller in engineering and research laboratories
• Analyst roles in environmental modelling, epidemiology or energy sectors
• Further academic study at the PhD level for those interested in research and university careers

The programme’s emphasis on applied projects and computational skills supports immediate employability and provides a pathway to doctoral study for research‑oriented students.

Why study at Canisius University

Canisius University offers a close‑knit learning environment with small class sizes and accessible faculty, which supports personalised mentoring and collaborative research opportunities. Its liberal arts and Jesuit tradition encourages ethical reflection alongside technical training, preparing graduates to consider societal impacts of quantitative work.

Located in the Buffalo region, the university provides access to local industries and public‑sector employers for internships and applied projects. Students benefit from university computing resources, opportunities to pursue interdisciplinary electives, and a supportive community that helps bridge classroom learning with professional experience.