Loyola University Chicago

What you'll study

This programme combines core quantitative theory with applied tools used in contemporary business analytics and decision support. Core topics typically include advanced statistics and probability, optimization and linear programming, stochastic processes, simulation, forecasting, and econometric methods. Students also cover data management and programming for analytics (for example using R, Python, SQL or similar), machine learning and data mining techniques, and decision analysis for managerial contexts.

Coursework is designed to be application-oriented: modules emphasise modelling real organisational problems, interpreting model outputs for managers, and communicating quantitative results to non‑technical stakeholders. Many students complete a capstone project, practicum or applied research paper in which they work with real datasets or industry partners to produce actionable insights.

• Advanced Statistical Methods and Applied Regression
• Optimization and Operations Research
• Simulation Modelling and Analysis
• Data Mining and Machine Learning for Business
• Forecasting and Time Series Analysis
• Decision Analysis, Risk and Uncertainty
• Database Management, Programming for Analytics
• Capstone/Applied Practicum or Research Project

Entry requirements

Applicants are normally expected to hold a recognised undergraduate degree. Candidates with degrees in mathematics, statistics, economics, engineering, computer science or related quantitative fields will be well prepared; applicants from other backgrounds should demonstrate quantitative aptitude through prior coursework or professional experience.

Typical supporting materials include a completed application form, official transcripts, a current CV or résumé, a personal statement explaining motivation and goals, and one or more academic or professional references. Standardised tests (such as the GRE or GMAT) may be required or optional depending on the admissions cycle; international applicants must also demonstrate English language proficiency according to the university’s requirements.

Successful applicants usually have foundational knowledge of calculus, linear algebra, introductory statistics and some exposure to programming or data analysis. Where gaps exist, bridging or preparatory courses may be recommended before starting the main programme.

Career prospects

Graduates move into analytical and decision-making roles across finance, consulting, healthcare, manufacturing, logistics, technology and the public sector. Common job titles include data scientist, quantitative analyst, operations research analyst, business analyst, supply chain analyst, risk analyst and analytics consultant.

The programme’s focus on applied projects and modelling prepares students for roles that require building, validating and communicating quantitative models to support strategy, operations and risk management. Graduates may work for large corporations, boutique analytics firms, financial institutions, healthcare systems, government agencies or start-ups.

Why study at Loyola University Chicago

Loyola University Chicago combines a strong academic environment with the practical advantages of being located in a major global business hub. The university emphasises experiential learning and ethical leadership, so quantitative skills are taught alongside considerations of responsible use, interpretation and communication of data.

Students benefit from access to faculty with applied research and industry experience, opportunities for practicum projects or internships with Chicago organisations, and university career services that support employer connections and professional development. The programme’s cohort size and classroom format also support close faculty interaction and collaborative team work—key skills for analytics professionals working in cross‑functional environments.

This curriculum is suitable for those seeking to deepen quantitative expertise and translate technical methods into impactful business and policy decisions.