Rochester Institute of Technology

What you'll study

The programme combines advanced quantitative theory with applied tools and industry-relevant projects. Core topics typically include optimisation (linear, integer and nonlinear programming), stochastic models and probabilistic analysis, simulation modelling, forecasting and time-series analysis, and applied statistics for decision making. Students also study decision analysis, risk management, data mining and predictive modelling, and database querying and programming for analytics (commonly using Python, R, or similar languages).

Coursework is complemented by applied components such as a practicum, industry project or capstone in which students solve real organisational problems using the techniques learned in class. Electives allow specialisation in areas such as supply chain analytics, revenue management and pricing, machine learning for business, or project and operations management. The curriculum emphasises model formulation, software implementation, interpretation of results for stakeholders, and communication of technically complex findings in practical terms.

Entry requirements

Applicants are expected to hold a bachelor's degree from an accredited institution, preferably in a quantitative discipline such as mathematics, statistics, engineering, computer science, economics or business. Typical supporting materials include official transcripts, a current CV or résumé, a statement of purpose describing quantitative background and career objectives, and one or more letters of recommendation.

Applicants who have not completed certain quantitative prerequisites (for example, calculus, linear algebra, probability and basic programming) may be asked to take bridging courses before or during the programme. Some form of standardised test (GRE/GMAT) may be required or optional depending on the applicant’s academic background; international applicants must demonstrate English language proficiency through an accepted test unless exempt.

Career prospects

Graduates are prepared for quantitative and analytical roles across sectors. Common positions include operations research analyst, data analyst/scientist, business/management consultant, supply chain or logistics analyst, pricing and revenue management analyst, risk analyst and decision analyst. Employers span manufacturing, retail, finance, healthcare, logistics, technology firms and consulting companies.

The programme’s emphasis on applied projects and software skills also supports transitions into analytics teams, product management roles that require strong quantitative literacy, and further study at the doctoral level in operations research or applied statistics.

Why study at Rochester Institute of Technology

RIT is known for an applied, career-focused approach to graduate education and strong links with industry. The institute offers extensive experiential learning opportunities, including cooperative education, practicum projects and partnerships with regional and national employers, which help students gain practical experience and build professional networks.

Students benefit from access to modern computing facilities, analytics and simulation labs, and interdisciplinary collaboration across business, engineering and computing schools. Small class sizes allow for close interaction with faculty who have applied research and industry experience. Located in a region with a diverse technology and manufacturing base, RIT provides strong career services and employer engagement to help graduates move into analytical and operational roles.