Clark University

What you'll study

The programme builds a strong core in pure and applied mathematics alongside specialised computational coursework. Early years typically cover calculus, multivariable calculus, linear algebra, differential equations and introductory probability and statistics. Mid-to-upper-level modules emphasise theoretical depth and computational practice, such as real analysis, abstract algebra, numerical analysis, scientific computing, numerical linear algebra and applied probability.

• Core mathematical foundations: single-variable and multivariable calculus, linear algebra, differential equations, real analysis.
• Computational and applied topics: numerical methods, computational modelling, numerical PDEs, optimisation, simulation and high-performance computing basics.
• Statistics and data-related courses: mathematical statistics, stochastic processes, data analysis and topics that bridge to data science.
• Computer science complements: programming, algorithms and data structures, databases or machine learning electives are commonly taken alongside mathematics modules.
• Capstone experience: senior seminar, project-based course or an independent senior thesis with a faculty supervisor that emphasises modelling, computation and applied problem solving.

Entry requirements

Applicants are expected to demonstrate strong achievement in secondary-level mathematics. Typical preparation includes completion of algebra, geometry, precalculus and, where available, calculus. Successful applicants usually show evidence of quantitative coursework and achievement in STEM subjects.

• Academic record: strong grades across rigorous coursework, with emphasis on mathematics and related sciences.
• Recommended preparation: Advanced Placement, International Baccalaureate or equivalent courses in calculus, statistics or programming where available.
• For international students: proof of English language proficiency by recognised testing if English is not the first language.
• Admission is holistic: personal statement, teacher recommendations and any relevant project or competition experience (math contests, research projects, coding portfolios) strengthen an application.

Career prospects

Graduates with computational mathematics training have broad career pathways in industry, government and academia. The combination of mathematical modelling and computational skills is particularly valued in data-driven roles.

• Data science and analytics roles: data scientist, data analyst, machine learning engineer.
• Finance and risk: quantitative analyst, actuary (with additional professional exam preparation), risk modeller.
• Software and technology: software engineer, algorithm developer, computational scientist.
• Research and engineering: modelling and simulation roles in engineering, climate science, biostatistics and computational biology.
• Further study: graduate study in mathematics, applied mathematics, computer science, statistics, operations research or related fields.

Why study at Clark University

Clark offers a small‑class liberal arts environment with a strong emphasis on undergraduate research and faculty mentorship, making it possible for undergraduates to engage in substantial computational and theoretical projects. Students benefit from interdisciplinary collaboration across mathematics, computer science and other applied departments, access to computing facilities and support for internships in the Worcester–Boston regional tech and research ecosystem.

The university’s career and internship services help connect students to real-world placements, and faculty-led research opportunities and senior theses provide practical experience that prepares graduates for both employment and further study.