Brandeis University

What you'll study

The programme blends theoretical foundations with hands-on applied work. Core topics include optimisation and linear programming, stochastic models and queuing theory, advanced statistics and econometrics, simulation modelling, and data management and visualisation. Students also learn programming and software tools commonly used in quantitative work, such as Python/R, SQL, and optimisation solvers.

• Core modules: Mathematical programming and optimisation; Probability and stochastic processes for decision making; Regression and advanced statistical inference; Simulation and risk analysis.
• Applied and elective modules: Machine learning for business analytics; Time-series forecasting; Supply chain and inventory models; Revenue management and pricing analytics; Network models and transportation.
• Capstone / practicum: A client-based practicum or applied project with an industry partner or faculty-led research group, producing a deliverable such as a predictive model, optimisation solution or data-driven decision-support tool.
• Workshops and labs: Hands-on computing labs, case studies in operations and finance, and seminars on ethics and data governance in quantitative practice.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree from an accredited institution. A strong quantitative background is required — commonly demonstrated through prior coursework in calculus, linear algebra, probability and statistics, or equivalent professional experience. Admissions typically evaluate the applicant’s academic transcripts, a statement of purpose, and letters of recommendation.

• Undergraduate degree in a quantitative discipline (e.g. mathematics, engineering, economics, computer science) or substantial quantitative coursework.
• Demonstrated programming or data-analytic experience is advantageous (Python, R, MATLAB, SQL or similar).
• Professional experience can strengthen an application but is not always required; fields such as consulting, analytics, finance, or operations are relevant.
• International applicants must meet English language proficiency requirements; supporting documents such as transcripts and recommendations are required.

Career prospects

Graduates move into analytical and decision-focused roles across industry, government and non-profit sectors. Typical job titles include Operations Research Analyst, Data Scientist, Business Analyst, Quantitative Analyst, Supply Chain Analyst and Management Consultant. The skill set is applicable in sectors such as technology, finance, healthcare, logistics and retail.

• Work in analytics and data science teams building predictive models and dashboards.
• Operational roles optimising supply chains, scheduling and resource allocation.
• Consulting roles advising organisations on data-driven strategy and efficiency improvements.
• Further academic study or research in operations research, applied statistics or related fields.

Why study at Brandeis University

Brandeis combines a strong quantitative curriculum with access to faculty who conduct applied research in operations, decision sciences and analytics. Situated near Boston, the university provides proximity to a vibrant ecosystem of technology, healthcare and finance employers, facilitating practicum projects, internships and industry connections. Small class sizes and personalised advising support close mentorship, while career services help translate technical skills into professional opportunities.

• Applied focus: Emphasis on projects and real-world problem solving with industry partners and research initiatives.
• Experienced faculty: Instructors with expertise in optimisation, statistics and analytics and active engagement in applied research.
• Location advantage: Access to the Boston-area analytics and technology community for networking and internships.
• Career support: Dedicated career resources to help students prepare for technical interviews, build portfolios and connect with employers.