Rochester Institute of Technology

What you'll study

The Applied Statistics curriculum builds a core of probability, statistical inference and regression, then expands into applied methods, computational statistics and domain-specific electives. Coursework emphasises hands-on projects, data analysis workflows and reproducible research practices.

• Core mathematics and probability: single-variable and multivariable calculus, linear algebra, discrete mathematics and probability theory.
• Core statistics: introductory and intermediate statistical inference, linear models, generalized linear models, experimental design and time series analysis.
• Computational and data skills: programming for data analysis (commonly Python and R), statistical computing, simulation and numerical methods, database fundamentals and data visualisation.
• Applied and elective areas: courses that apply statistics to areas such as biostatistics, industrial statistics and quality control, environmental statistics, machine learning and survey methodology.
• Capstone and experiential learning: a senior capstone or practicum project requiring students to manage and analyse real datasets; opportunities for co-operative education placements and research with faculty.

Students typically take a balance of theoretical and applied courses across the major, complete supporting coursework in computing and mathematics, and select electives or minors to specialise in an application area such as finance, engineering, public health or computer science.

Entry requirements

Applicants should hold a high-school diploma or equivalent with strong preparation in mathematics. Admissions favour students who have completed calculus and statistics in secondary school and who demonstrate aptitude for quantitative reasoning.

• Recommended preparatory subjects: calculus (single-variable at minimum), algebra, trigonometry and introductory statistics.
• Programming experience is advantageous: prior exposure to a programming language (for example Python, R, Java or MATLAB) strengthens an application but is not strictly required.
• Typical application materials: academic transcripts, a personal statement describing interest in statistics and quantitative work, and letters of recommendation. International applicants should consult the university for English language proficiency requirements.

Career prospects

Graduates of the Applied Statistics programme are prepared for quantitative roles across many sectors. The degree develops skills in data analysis, statistical modelling and computational tools that employers value.

• Common entry roles: data analyst, statistical analyst, quality analyst, business analyst and research assistant.
• Sector opportunities: finance and insurance, healthcare and biostatistics, manufacturing and industrial analytics, technology and software companies, government agencies and non-profit research organisations.
• Further study and professional pathways: many graduates pursue master’s degrees in statistics, data science, biostatistics, operations research or analytics, or professional certification in analytics and related software tools. The programme also provides solid preparation for actuarial examinations with additional coursework.

Why study at Rochester Institute of Technology

RIT is known for its emphasis on experiential learning and strong industry connections; the university’s co-operative education programme gives students the opportunity to complete paid, curriculum-integrated work placements that build practical skills and professional networks. The institute’s interdisciplinary environment encourages collaboration between statistics, computer science, engineering and business, allowing students to tailor their studies to particular application areas.

• Applied focus: curriculum designed to balance statistical theory with hands-on data analysis, computation and project-based learning.
• Industry engagement: access to co-op placements, internships and employer partnerships in the tech, manufacturing and healthcare sectors.
• Facilities and resources: computing resources, statistical software support and faculty with applied research and consulting experience.
• Location and community: proximity to a regional hub of companies and research institutions that hire graduates in analytics and related roles.