Rochester Institute of Technology

What you'll study

The programme emphasises hands-on training in the full analytics lifecycle: acquiring and cleaning data, building predictive models, deploying analytics systems and communicating results to stakeholders. Core topics typically include statistical inference and applied regression, machine learning, data mining, large-scale data processing, database systems, data visualisation and ethical/legal issues in analytics. Coursework balances theory and application through lab exercises, team projects and a culminating capstone or practicum with an industry partner.

• Statistical Methods and Applied Regression — foundations in probability, hypothesis testing and regression techniques for real-world datasets.
• Machine Learning and Predictive Analytics — supervised and unsupervised learning methods, model selection and evaluation.
• Big Data Technologies and Data Engineering — architectures and tools for storage, processing and ETL at scale.
• Databases and Data Management — relational and non-relational systems, query languages and data modelling.
• Data Visualisation and Communication — principles and tools for presenting analytic results to technical and non-technical audiences.
• Ethics, Privacy and Governance — responsible use of data, bias mitigation and regulatory considerations.
• Capstone/Practicum or Internship — team-based project solving a real analytics problem, often developed in collaboration with industry.

Entry requirements

Applicants are expected to hold a bachelor's degree from an accredited institution. While degrees in computer science, mathematics, statistics, engineering, economics or related quantitative fields are a natural fit, applicants from other disciplines with strong quantitative coursework and relevant experience may also be considered.

• Academic transcript(s) demonstrating prior study in calculus, linear algebra, probability/statistics or equivalent.
• Proficiency in programming (e.g. Python, R, or equivalent) or a willingness to complete foundation modules if required.
• A current résumé/CV outlining academic and professional experience.
• A personal statement describing objectives and relevant experience.
• Letters of recommendation, typically one to three, from academic or professional referees.
• Proof of English language proficiency for applicants whose first language is not English (accepted tests vary by institution policy).

Some applicants may be asked to complete preparatory coursework before or during the programme if they lack foundational skills in programming or mathematics. Standardised tests (such as the GRE) may be optional or considered on a case-by-case basis depending on programme admissions policies.

Career prospects

Graduates leave prepared for roles that bridge technical analytics and business decision-making. Typical job titles include:

• Data Analyst — analysing business data, building dashboards and producing actionable reports.
• Data Scientist — developing predictive models and machine-learning solutions to extract insights from complex datasets.
• Data Engineer — designing and maintaining data pipelines and infrastructure for large-scale analytics.
• Business Intelligence Analyst / Analytics Consultant — translating analytic findings into strategic recommendations for organisations.
• Machine Learning Engineer — deploying models into production and optimising performance at scale.

Graduates commonly find opportunities across sectors such as finance, healthcare, technology, retail, manufacturing and government. The programme’s emphasis on applied projects and industry collaboration supports readiness for professional practice and continued study.

Why study at Rochester Institute of Technology

RIT offers a career-oriented learning environment with strong ties to industry and a focus on experiential education. Students benefit from access to specialised computing facilities, research centres and collaborative projects with corporate and civic partners. The institute’s emphasis on cooperative education and applied internships gives students practical experience and professional networking opportunities that complement classroom learning. Additionally, interdisciplinary resources across computing, business and engineering help students apply analytics in diverse contexts.