Georgia Institute of Technology

What you'll study

The programme focuses on mathematical modelling and computational methods used to make data-driven decisions in complex operational settings. Core subject areas include deterministic optimisation (linear and integer programming), nonlinear optimisation, stochastic processes and stochastic optimisation, probability and statistical modelling, simulation, and computational methods for large-scale problems. Students also study applications such as supply chain and logistics, queuing and service systems, revenue management, and data-driven decision making.

Teaching is delivered through a combination of lectures, problem sets, programming assignments and projects. Students typically take a set of required core courses to build a rigorous theoretical foundation, then choose electives that emphasise applications or advanced theory depending on their career goals. Many students complete a substantial applied project or research thesis under faculty supervision; there are both coursework-focused and research-oriented pathways.

• Typical modules: linear and integer programming, nonlinear optimisation, stochastic processes, stochastic optimisation, simulation, statistical inference for OR, computational methods, and application-focused electives (e.g. supply chain analytics, machine learning for decision-making, queuing theory).
• Practical components: numerical computation and programming (commonly in languages such as Python, MATLAB or R), team projects with real datasets, and options for internship or industry practicum.
• Study length and format: the programme is offered full-time and can be completed in one to two years depending on course load and whether a thesis or project is chosen.

Entry requirements

Applicants are expected to have a strong undergraduate background in mathematics, engineering, operations research, computer science, applied statistics or a closely related field. Foundational coursework in calculus, linear algebra, probability/statistics and programming is typically required. Admissions materials generally include a transcript, statement of purpose, résumé or CV, and letters of recommendation.

International applicants must demonstrate English proficiency through an approved test unless exempt. Some applicants may be asked to supply additional evidence of quantitative preparation or to complete prerequisite coursework if their prior degree does not cover core mathematical foundations.

Career prospects

Graduates of the programme pursue careers where optimisation and quantitative decision-making are central. Common roles include operations research analyst, optimisation engineer, data scientist, supply chain analyst, logistics and transportation planner, quantitative consultant, and roles in finance or revenue management. Employers include technology and software companies, logistics and manufacturing firms, consulting firms, financial institutions, healthcare systems and government agencies.

Graduates benefit from Georgia Tech’s connections with industry in Atlanta and beyond, which support internship and employment opportunities. The programme’s strong computational and modelling emphasis also provides a foundation for continuing to doctoral study in operations research or related disciplines.

Why study at Georgia Institute of Technology

Georgia Tech’s programme is housed within a recognised engineering school with a long-standing strength in industrial and systems engineering and operations research. Students have access to faculty who are active in theoretical and applied research, and to interdisciplinary centres and labs that focus on data science, optimisation and systems analytics.

The campus location in Atlanta provides proximity to a concentration of industry partners across logistics, technology, finance and consulting, enabling internship and collaboration opportunities. Strong computing facilities, collaborative research groups and a curriculum that emphasises both rigorous theory and hands-on application make the programme well suited to students who want to apply advanced quantitative methods to real-world operational problems.