John Hopkins University

What you'll study

The curriculum blends core mathematical theory with probability and statistical methods. Early coursework normally includes calculus (single-variable and multivariable), linear algebra and introductory proof-based mathematics. Students progress to intermediate and advanced courses such as real analysis, abstract algebra, differential equations and numerical analysis alongside probability theory and mathematical statistics.

• Foundations: Calculus, Multivariable Calculus, Linear Algebra
• Core theory: Real Analysis, Abstract Algebra, Topology (elective)
• Probability and Statistics: Probability Theory, Mathematical Statistics, Regression and Design of Experiments
• Applied and computational: Numerical Methods, Mathematical Modelling, Computational Statistics, Data Science and Machine Learning electives
• Capstone and research: Senior seminar, independent study, or an optional senior thesis under faculty supervision

Students are encouraged to take computing courses to develop programming skills (for example in Python, R or MATLAB), and to pursue electives that reflect interdisciplinary interests, such as courses in economics, computer science, engineering or public health. The programme often offers opportunities for undergraduate research and collaboration with faculty on applied projects.

Entry requirements

Admissions are selective. Typical academic preparation expected includes strong performance in mathematics through the highest available secondary level (for example multivariable calculus and linear algebra where offered), and a solid record across science and quantitative subjects. Successful applicants usually demonstrate:

• High school grades in demanding courses, particularly in mathematics
• Evidence of problem-solving ability and mathematical maturity, such as performance in advanced mathematics courses or national/international mathematics competitions (if applicable)
• Preparation in writing and communication skills, because proof-based courses require clear mathematical exposition
• Recommended: prior exposure to programming or statistics is advantageous but not always required

Specific application materials and criteria are provided by the university; prospective students should consult the official undergraduate admissions guidance for complete details on tests, transcripts and supplementary materials.

Career prospects

Graduates with a degree in Mathematics and Statistics have wide career options because the training develops analytical thinking, quantitative modelling and data literacy. Common career paths include:

• Data science and analytics roles across technology, retail, and healthcare sectors
• Quantitative finance, risk analysis and actuarial work
• Software engineering and algorithm development
• Biostatistics and public-health analytics, particularly relevant given Johns Hopkins’ strong public health and medical community
• Academic and industrial research or further study in graduate programmes (MS/PhD) in mathematics, statistics, data science, economics or engineering

Undergraduates frequently secure internships and positions with local research groups, hospitals, government agencies and companies in the Baltimore–Washington corridor, and many go on to competitive graduate programmes.

Why study at Johns Hopkins University

Johns Hopkins offers a rigorous academic environment with strong emphasis on research and interdisciplinary collaboration. Mathematics and statistics students benefit from access to faculty who are active researchers, opportunities to work on applied problems with departments such as public health, medicine, engineering and computer science, and a range of centres and institutes across the university that frequently engage undergraduates.

• Research opportunities: undergraduates can take part in faculty-led research projects and independent theses.
• Interdisciplinary links: easy access to applied problems in public health, biology, economics and engineering.
• Professional development: career services, internship connections locally and nationally, and support for applications to graduate programmes.

Overall, the programme is designed to prepare students for both immediate entry into quantitative careers and for advanced study, combining theoretical depth with applied and computational experience.