Rochester Institute of Technology

What you'll study

The programme combines theoretical foundations in probability and inference with hands-on training in computational methods, data management and applied modelling. Core topics typically include statistical inference, linear and generalized linear models, multivariate analysis, experimental design, time series and forecasting, and Bayesian methods.

• Core modules: probability and statistical inference; regression methods; multivariate statistics; design of experiments; statistical computing and simulation.
• Applied and computational modules: data mining and machine learning, time series analysis, Bayesian data analysis, high-dimensional data and regularisation, resampling and bootstrap methods.
• Practical experience: a capstone project, practicum or consulting course that emphasises real datasets, reproducible workflows and communication of results to non‑technical stakeholders. Students often use statistical software such as R and Python and have access to high-performance computing resources.
• Electives and cross-disciplinary options: electives allow specialisation in areas such as biostatistics, industrial statistics, analytics for business, or computational sciences, and students can take relevant courses from computer science, engineering or business to tailor their training.

Programme delivery is focused on applied problem solving and reproducible practice; students normally complete a mix of coursework and a culminating applied project or thesis depending on the chosen track.

Entry requirements

• Bachelor's degree from an accredited institution in statistics, mathematics, engineering, computer science, economics or a related quantitative field.
• Demonstrated preparation in calculus, linear algebra and introductory probability and statistics; additional coursework in programming or data structures is advantageous.
• Application materials generally include official transcripts, a statement of purpose outlining academic and professional goals, a current résumé or CV, and one or more letters of recommendation.
• International applicants must demonstrate English language proficiency through recognised tests or institutional exemptions; documentation of prior academic credentials is required.
• Admission decisions consider quantitative background and preparedness for graduate-level work; applicants without direct statistics background may be admitted conditional on completing prerequisite courses.

Career prospects

Graduates are prepared for roles that require strong statistical reasoning and data analysis skills. Typical career paths include:

• Data scientist or data analyst in technology, finance, manufacturing or retail
• Statistician or quantitative analyst in government agencies, healthcare and pharmaceuticals
• Biostatistician or clinical data analyst in life sciences and medical research
• Analytics consultant, market researcher or operations analyst in industry
• Further study and research roles, including doctoral programmes in statistics, biostatistics or related fields

Students benefit from RIT's career services, industry partnerships and optional experiential learning opportunities that support internship and employment pathways.

Why study at Rochester Institute of Technology

• Applied focus: RIT emphasises practical, project-based learning that integrates statistical theory with computing and industry-relevant applications.
• Strong computing resources: access to modern statistical software, programming environments and high-performance computing facilities supports work with large and complex datasets.
• Industry connections and experiential learning: ties with local and national employers, co‑op and internship opportunities, and interdisciplinary collaborations enable students to gain workplace experience and professional networks.
• Flexible curriculum: elective choices and cross-college courses let students tailor their degree toward business analytics, biostatistics, machine learning or industrial applications.
• Support services: dedicated faculty mentorship, career services and technical workshops help students transition into analytical careers or further academic study.