Rice University

What you'll study

This interdisciplinary degree focuses on the theory and practice of quantitative decision‑making. Core subjects typically include calculus and linear algebra, probability and mathematical statistics, optimisation and operations research, econometrics, and data structures and programming. Students learn methods for modelling and solving business problems: deterministic and stochastic optimisation, simulation, statistical modelling, machine learning techniques for prediction, and causal inference for policy evaluation.

Coursework balances theory and application. Typical modules cover:

• Mathematical foundations: calculus, linear algebra and numerical methods that underpin modelling.
• Probability & statistics: probability theory, statistical inference, regression and time‑series analysis.
• Optimisation & operations research: linear, integer and nonlinear programming, network models and stochastic processes.
• Computational methods & data science: programming (Python/R), databases, data visualisation, machine learning and simulation.
• Business applications: decision analysis, supply‑chain analytics, revenue management, financial engineering and applied econometrics.

Students often complete a capstone project or practicum that applies quantitative methods to a real organisational problem, and can tailor their studies through electives or minors in areas such as computer science, economics, finance, or entrepreneurship.

Entry requirements

Admission is through Rice University’s undergraduate admissions process and is selective. Successful applicants demonstrate strong academic preparation, especially in mathematics (calculus) and quantitative reasoning, and show evidence of analytical aptitude. Preparation in programming and statistics is highly recommended.

Typical application materials include academic transcripts, essays or personal statements, and letters of recommendation. Rice considers the whole application; extracurricular achievements, research experience, internships or maths/analytics competitions can strengthen an application. Standardised testing policies may vary, so consult Rice’s admissions guidance for current requirements and recommended preparatory coursework.

Career prospects

Graduates with this degree are equipped for roles that require rigorous quantitative skills and business understanding. Common career paths include data analyst/scientist, operations research or supply‑chain analyst, management consultant, financial analyst or quantitative risk analyst, product analyst or business intelligence specialist. Graduates also move into technology and software roles where analytics and optimisation are core.

Many alumni pursue further study such as specialised master’s degrees in analytics, operations research, statistics or finance, or business school for an MBA. The programme’s emphasis on applied projects and internships helps students build portfolios and employer connections across finance, energy, healthcare, consulting and technology sectors—particularly leveraging Rice’s strong links to the Houston business community and national internship networks.

Why study at Rice University

Rice offers small class sizes and close faculty mentoring that suit an intensive, technical major. The university’s collaborative undergraduate culture gives students access to interdisciplinary resources across departments—combining quantitative rigour with business and economics perspectives. Students benefit from research opportunities with faculty and industry‑facing centres, plus entrepreneurship support through campus initiatives.

Located in Houston, Rice provides proximity to a diverse economy—energy, healthcare, finance and a growing tech scene—creating strong internship and employment pipelines. The department’s emphasis on applied work, coupled with Rice’s career services and alumni network, helps students translate quantitative skills into immediate workplace impact or advanced study.