John Hopkins University

What you'll study

This programme builds a strong foundation in pure mathematics while emphasising computational techniques used across science and engineering. Core topics typically include multivariable calculus, linear algebra, differential equations and real analysis, followed by computational modules such as numerical analysis, numerical linear algebra, scientific computing and algorithms for large-scale problems.

• Core mathematics: calculus sequence, linear algebra, ordinary differential equations, real analysis and probability theory.
• Computational and applied modules: numerical analysis, numerical methods for PDEs, numerical linear algebra, scientific computing, optimisation and computational modelling.
• Complementary subjects: programming (Python, C/C++ or MATLAB), data structures and algorithms, statistics, machine learning and computational statistics.
• Advanced theory and electives: complex analysis, abstract algebra, topology and specialised electives depending on research interests (e.g. computational biology, computational finance, image processing).
• Capstone and research: options for a senior thesis, honours project or participation in faculty-led research groups; opportunities for interdisciplinary projects with departments such as Computer Science, Physics, Biology and Engineering.

Entry requirements

Applicants are expected to demonstrate strong quantitative preparation. Typical successful candidates have strong performance in secondary-level mathematics (for example A-levels, IB Higher Level maths or equivalent). Prior exposure to calculus and some programming experience strengthens an application. Johns Hopkins evaluates candidates holistically, considering academic transcripts, recommendation letters, and evidence of quantitative problem-solving such as coursework, competitions or projects.

For transfer applicants or those from different educational systems, preparation in multivariable calculus, linear algebra and introductory programming will be important for placement into appropriate first-year courses.

Career prospects

Graduates with a computational mathematics bachelor’s degree are well placed for careers that require strong analytical, numerical and programming skills. Common career paths include:

• Data scientist or data analyst roles in technology, finance and healthcare.
• Software engineering and algorithm development, particularly for scientific or high-performance computing applications.
• Quantitative analyst (quant) positions in finance and risk management.
• Computational scientist or modeller in engineering, climate science, computational biology and related research labs.
• Further study: many graduates proceed to graduate programmes in applied mathematics, computer science, statistics, engineering or operations research.

Why study at Johns Hopkins University

Johns Hopkins offers access to a research-intensive environment where computational mathematics is closely integrated with applied disciplines. Students benefit from collaboration possibilities with the Whiting School of Engineering, the Department of Computer Science, and domain-focused centres such as computational medicine and data science institutes.

The university provides resources valuable to computational students, including high-performance computing facilities, opportunities to contribute to active research projects, and a strong network for internships and industry engagement in the Baltimore–Washington region. Faculty mentoring, undergraduate research programmes and interdisciplinary coursework help students tailor the degree toward academic research or professional careers in computing and quantitative analysis.