Slippery Rock University

What you'll study

The programme emphasises numerical methods, algorithm development and mathematical modelling, combining rigorous theoretical foundations with hands-on computational work. Students follow a mix of core courses and electives, and typically choose between a thesis/research option and a project/non-thesis route.

• Core topics: advanced numerical analysis, numerical linear algebra, scientific computing, ordinary and partial differential equations, and mathematical modelling techniques.
• Typical electives: optimization and computational optimisation, computational statistics and data analysis, high-performance computing, finite element and finite difference methods, stochastic modelling and uncertainty quantification, and applied linear algebra.
• Programming and tools: instruction and practice in languages and environments commonly used in computational mathematics, such as MATLAB, Python (NumPy/SciPy), and parallel computing frameworks.
• Research and project work: a significant component is independent work — either a supervised thesis involving original research or a substantial computational project that applies numerical methods to a concrete problem in science, engineering or industry.

Entry requirements

Applicants should hold a bachelor’s degree in mathematics, applied mathematics, engineering, physics, computer science or a closely related field, with evidence of strong preparation in calculus, linear algebra and proof-based courses. Departments set specific academic standards; applicants normally submit official transcripts, a personal statement outlining their background and goals, and academic references. GRE requirements vary by department and are not universally required; international applicants must demonstrate English proficiency through recognised tests unless exempt.

Career prospects

Graduates are prepared for a broad range of roles that require advanced quantitative and computational skills. Common career paths include:

• Data scientist or data analyst in industry and research organisations.
• Computational scientist or numerical analyst in engineering firms, research labs and technology companies.
• Quantitative analyst or model developer in finance, insurance and risk management.
• Software developer or algorithm engineer focusing on scientific or numerical applications.
• Continuation to doctoral study in applied or computational mathematics, statistics or related fields, and academic careers.

Why study at Slippery Rock University

Slippery Rock University offers a close-knit graduate environment with accessible faculty who are active in applied and computational research. Students benefit from small class sizes, opportunities for one-to-one mentoring, and access to departmental computing resources for numerical experimentation and modelling. The programme’s balance of theory, programming and project experience makes it well suited to students aiming for industry roles or further research, and graduate assistantships and teaching opportunities provide practical professional experience and financial support options.