Georgia Institute of Technology

What you'll study

This programme combines the core mathematics curriculum with a suite of computational and applied courses. You will build a foundation in calculus, linear algebra, differential equations and real analysis, and then move into computational topics such as numerical analysis, numerical linear algebra, partial differential equations, scientific computing and algorithmic methods for large-scale problems.

• Core mathematics: multivariable calculus, linear algebra, ordinary differential equations, real analysis and abstract algebra.
• Computational and applied modules: numerical analysis, numerical linear algebra, computational methods for PDEs, scientific computing, numerical optimisation and computational modelling.
• Programming and computing: practical training in programming languages commonly used in scientific computing (for example Python, MATLAB and C/C++), data structures and algorithms, and exposure to high-performance and parallel computing concepts.
• Probability and statistics: probability theory, mathematical statistics and applied statistical methods useful for data analysis and stochastic modelling.
• Electives and interdisciplinary options: courses from computer science, engineering, physics or data science to tailor the degree toward interests such as machine learning, computational physics or financial mathematics.
• Research and project work: opportunities for undergraduate research projects, team-based computational capstones or independent study under faculty supervision, emphasising model development, algorithm implementation and validation on real data.

The degree typically requires completion of a set of mathematics core requirements plus a concentration of computational electives and general education requirements. Practical laboratory and computing coursework is integrated throughout to ensure students graduate with both theoretical understanding and hands-on skills.

Entry requirements

Admission to Georgia Tech is competitive. Applicants are expected to demonstrate strong achievement in mathematics and related STEM subjects at secondary level. Typical applicants will have completed advanced mathematics courses such as calculus; further preparation in physics or computer science is advantageous.

• Academic profile: evidence of high academic performance in secondary school with significant emphasis on mathematics and quantitative subjects.
• Preparatory coursework: completion of calculus and, where available, courses in statistics, discrete mathematics or introductory programming strengthens an application.
• Programming experience: prior experience with one or more programming languages and exposure to algorithmic thinking is beneficial for success in computational modules.
• Standardised testing and English language: applicants should meet any testing and English language proficiency requirements specified by the university; details are available from the admissions office.
• Other factors: strong letters of recommendation, a thoughtfully prepared personal statement and evidence of extracurricular engagement in mathematics, computing or research can support an application.

Career prospects

Graduates with a computational mathematics degree are well positioned for a broad range of careers where quantitative modelling, algorithm development and data-driven decision making are central. The combination of rigorous mathematics and practical computing prepares students for technical and analytical roles across sectors.

• Data scientist, data analyst or machine learning engineer in technology and finance.
• Quantitative analyst or developer roles in finance and risk management.
• Software engineer or computational scientist in engineering, scientific research and product development.
• Research positions in academia, government laboratories or industrial research groups, often following further graduate study.
• Roles in modelling and simulation for energy, aerospace, materials science, and environmental modelling.
• Consulting and analytics roles where strong mathematical modelling and programming skills are required.

Why study at Georgia Institute of Technology

Georgia Tech is known for its strong emphasis on technology and applied research, making it a natural home for students interested in computational mathematics. The School of Mathematics works closely with departments such as the College of Computing and engineering schools, providing interdisciplinary pathways and access to applied problems and internships.

• Interdisciplinary opportunities: collaboration with computing, engineering and data science disciplines allows students to apply mathematical tools to real-world problems and interdisciplinary research projects.
• Research and computing resources: undergraduates can take advantage of faculty-led research groups, computing facilities and opportunities to work with high-performance computing resources.
• Industry connections and location: proximity to a dynamic technology and business ecosystem provides strong internship and employment prospects with major companies and research organisations.
• Career support: the institute's career services and strong alumni network help translate technical training into internships and graduate destinations.