Dartmouth University

What you'll study

This programme combines core mathematical theory with computational methods and practical programming. Early years emphasise foundational subjects such as single- and multivariable calculus, linear algebra, differential equations and introductory probability and statistics. Intermediate and advanced modules typically include real analysis, abstract algebra, numerical analysis, scientific computing, computational linear algebra, optimisation and mathematical modelling.

Computationally focused courses train students in algorithmic thinking, numerical simulation, data structures and software for high-performance computation. Elective options often cover topics such as machine learning, computational geometry, stochastic processes, partial differential equations, and statistical computing. Many students undertake an advanced project or senior thesis involving original computational work, and the curriculum encourages the use of modern tools (for example Python, MATLAB, R or C++) and parallel/HPC environments in coursework and research.

Entry requirements

Admission to Dartmouth's undergraduate mathematics programme is selective and based on holistic review. Successful applicants typically demonstrate strong performance in high-school mathematics (including calculus where available), solid preparation in related sciences and computer science, and evidence of problem-solving ability. Typical preparation includes rigorous coursework (A-levels, IB higher level or advanced placement, where applicable) in mathematics and a programming class or constructive experience with computational projects.

Applicants should present strong academic records, teacher recommendations that speak to quantitative ability, and examples of mathematical engagement such as competition experience, research projects or independent work. Specific grade thresholds, test policies, and application components follow the institution's overall undergraduate admissions process.

Career prospects

Graduates with a computational mathematics major have a broad range of career paths. Common destinations include quantitative roles in finance (quantitative analyst, risk modeller), data science and analytics, software engineering, scientific research and engineering, operations research, and roles at technology companies focusing on algorithms and machine learning. The combination of rigorous mathematics and computational skills is also excellent preparation for graduate study in applied mathematics, computer science, statistics, data science, engineering, or economics.

Many students secure internships and entry-level positions with tech firms, research labs, consulting firms and government agencies; others pursue teaching or academic careers after further study. Alumni networks and career services at Dartmouth support recruitment into both industry and postgraduate programmes.

Why study at Dartmouth University

Studying computational mathematics at Dartmouth offers the advantages of a liberal-arts undergraduate environment with close faculty interaction, small seminar-style classes at advanced levels, and opportunity for supervised undergraduate research. The department's emphasis on both theoretical depth and computational practice gives students flexibility to pursue interdisciplinary work with departments such as computer science, physics, engineering and economics.

Students benefit from accessible faculty mentorship, opportunities for senior theses or research assistantships, and a collegiate setting that encourages collaboration. On-campus resources such as computing facilities, specialised labs and career services help bridge classroom learning with internships and research experiences that prepare graduates for technical careers or further study.