Georgetown University

What you'll study

The programme combines core training in mathematical modelling and statistics with applied coursework in optimisation, stochastic processes and data-driven decision making. Typical modules cover:

• Mathematical Foundations: multivariate calculus, linear algebra and optimisation theory for modelling organisational problems.
• Probability and Statistical Inference: probability theory, statistical estimation, hypothesis testing and regression methods.
• Operations Research and Optimisation: linear and nonlinear programming, integer programming, network flows and heuristics for large-scale problems.
• Stochastic Models and Simulation: queueing theory, Markov chains, Monte Carlo simulation and inventory models.
• Applied Econometrics and Forecasting: time-series methods, causal inference and forecasting techniques used in business and policy contexts.
• Computational Methods and Machine Learning: programming for data analysis (commonly Python or R), supervised and unsupervised learning, and scalable algorithms for large datasets.
• Data Visualisation and Communication: presenting quantitative results to non-technical stakeholders and building dashboards for decision support.
• Capstone or Applied Project: a hands-on project with an industry, government or research partner that integrates modelling, analysis and implementation of solutions.

The programme typically allows elective options so students can specialise in areas such as supply chain analytics, financial engineering, healthcare operations or public sector analytics. Coursework emphasises a mix of theory, computational practice and real-world case studies.

Entry requirements

Applicants are expected to hold a bachelor’s degree from a recognised institution. Successful candidates typically have a strong quantitative background—examples include degrees in mathematics, economics, engineering, statistics, computer science or related disciplines. Typical application components include:

• Academic transcripts demonstrating quantitative coursework (calculus, linear algebra, probability/statistics).
• A current CV or résumé outlining relevant academic, research or professional experience.
• A personal statement describing fit with the programme, quantitative interests and career goals.
• Letters of recommendation from academic or professional referees who can speak to analytical skills and potential for graduate study.
• Proof of English language proficiency for applicants whose first language is not English.

Some applicants may be asked to submit standardised test scores (such as the GRE) or evidence of programming experience. Admissions committees look for demonstrated quantitative aptitude, problem‑solving ability and readiness for a fast-paced, technical curriculum.

Career prospects

Graduates of this programme enter roles that require advanced analytical and quantitative skills. Common career paths include:

• Data scientist or data analyst in technology, finance, consulting and healthcare organisations.
• Operations research analyst or optimisation specialist working on supply chain, logistics and production planning.
• Quantitative analyst in financial services, risk management or algorithmic trading.
• Management consultant focused on analytics-driven strategy and operational improvement.
• Policy analyst or quantitative researcher in government agencies, think tanks and international organisations.

Work placements, internships and the capstone project help students build a portfolio of applied work and industry contacts. Graduates are prepared for roles that bridge technical modelling and practical decision making.

Why study at Georgetown University

Studying this programme at Georgetown provides access to faculty with expertise in quantitative methods, operations research and applied analytics, and situates students in Washington, D.C., a hub for government agencies, international organisations and consulting firms. The university’s interdisciplinary environment enables collaboration across business, public policy and technology disciplines, while experiential learning opportunities allow students to test methods on real problems. Georgetown’s alumni network and connections to employers in both the public and private sectors support career development and recruiting for analytically focused roles.