University of Tulsa

What you'll study

This programme centres on applied quantitative techniques used to support managerial decision making. Core topics typically include optimisation and linear programming, stochastic models and simulation, advanced statistics and regression, time series forecasting, and decision analysis. Students also study data management and analytics, programming for quantitative analysis (commonly using languages such as R and Python), and applied econometrics.

Courses are structured to combine theory with hands-on projects. Typical modules you can expect are:

• Operations Research and Optimisation – linear and integer programming, network models, multi-objective optimisation.
• Applied Statistics and Regression – inference, multivariate methods, model selection and diagnostics.
• Time Series and Forecasting – ARIMA, exponential smoothing, demand forecasting techniques.
• Stochastic Models and Simulation – queuing theory, Monte Carlo simulation and applications to service and inventory systems.
• Data Analytics and Machine Learning – supervised and unsupervised learning methods for prediction and classification in business contexts.
• Database and Programming for Analytics – data manipulation, SQL, scripting for reproducible analysis.
• Capstone Project or Thesis – an applied project with a real dataset or a research thesis that integrates the quantitative skills learned.

The programme emphasises practical applied work: case studies, industry datasets, and team projects are commonly used to build skills employers seek. Elective options may allow focus on areas such as supply chain analytics, financial risk modelling, or healthcare operations.

Entry requirements

Applicants are normally expected to hold an accredited bachelor’s degree in a relevant discipline such as business, economics, mathematics, statistics, engineering or a related field. Strong quantitative preparation (calculus, linear algebra, introductory statistics) is usually required. Admissions decisions consider academic transcripts, a statement of purpose, a current résumé or CV, and one or more letters of recommendation.

Standardised test requirements (GRE/GMAT) may be considered depending on an applicant’s academic background; check the programme office for current testing policy. International applicants must demonstrate English language proficiency with an accepted test unless exempt under the university’s criteria.

Relevant work experience is beneficial but not always required; candidates with strong quantitative skills but limited professional experience are still competitive when they demonstrate potential through coursework, projects or employer references.

Career prospects

Graduates enter roles that require advanced quantitative and problem‑solving skills. Common job titles include operations analyst, data analyst, business intelligence analyst, supply chain analyst, pricing analyst, risk analyst and management consultant. Employers are found across finance, manufacturing, energy, healthcare, retail and technology sectors, as well as at consulting firms and government agencies.

The degree also provides a strong foundation for continued academic study at doctoral level for those interested in research careers in operations research, statistics, economics or management science.

Why study at University of Tulsa

The University of Tulsa offers a learning environment with small class sizes and direct access to faculty who combine academic expertise with applied research. The programme’s emphasis on practical, project-based learning prepares students to apply quantitative methods to real business problems. Tulsa’s regional economy, with strengths in energy and manufacturing as well as growing technology and logistics sectors, creates local industry connections and internship opportunities.

Students benefit from campus computing facilities, statistical and analytics software access, and the university’s career services that help with employer networking, interview preparation and internship placement. The programme’s flexibility often allows both full-time study and options for part-time or professional students seeking to upskill while working.