CUNY(The City University of New York)

What you'll study

The programme combines rigorous core training in probability and statistical inference with applied coursework tailored to biomedical problems. Students take core modules in probability theory, statistical inference, linear models and computational statistics, then specialise through biostatistics-focused subjects and electives.

• Core subjects: Probability and measure-theoretic foundations (or advanced probability), Statistical inference and likelihood-based methods, Linear models and regression, Applied computational statistics and programming (R/Python/SAS).
• Biostatistics-focused modules: Survival analysis and time-to-event methods, Longitudinal and repeated-measures analysis, Categorical data analysis, Design and analysis of clinical trials, Epidemiologic methods for causal inference.
• Advanced and elective topics: Bayesian methods for biostatistics, High-dimensional data and penalised regression, Genomic and bioinformatics data analysis, Missing data and multiple imputation, Machine learning applications in health data.
• Practical experience: Statistical consulting practicum or internship with a hospital, public health agency, research lab or industry partner; capstone project or master’s thesis involving real biomedical data and reproducible analysis.

Typical structure

Students usually complete a mix of core courses, electives and a culminating project over one to two academic years of full-time study. The pathway may allow either a project/capstone route for applied training or a thesis route for students aiming at research or PhD programmes.

Entry requirements

Successful applicants normally hold a bachelor’s degree in statistics, mathematics, computer science, engineering, economics, or a quantitative life-science discipline. Admissions emphasise demonstrated quantitative preparation and programming ability.

• Academic transcripts showing coursework in calculus (including multivariable), linear algebra, probability and mathematical statistics or equivalent.
• Evidence of computing skills such as programming experience in R, Python or SAS and familiarity with data analysis.
• Application materials: personal statement describing quantitative background and research/career goals, curriculum vitae, and academic references. Some applicants may be asked to submit examples of quantitative work or a coding sample.
• International applicants must demonstrate English language proficiency according to the university’s standard admissions policies.
• Conditional admission pathways or prerequisite courses may be offered for applicants who lack some mathematical coursework.

Career prospects

Graduates are prepared for roles across healthcare, industry and government where statistical expertise is essential. The degree is explicitly aimed at applications in biomedicine and public health.

• Typical job titles: biostatistician, clinical trial statistician, data scientist or analyst in healthcare, epidemiology data analyst, statistical consultant, research analyst in biotech or pharmaceutical companies.
• Employment sectors: hospitals and health systems, academic research centres, public-health agencies, pharmaceutical and medical-device companies, contract research organisations (CROs), health-technology startups and non-profit research organisations.
• Further study: many graduates pursue doctoral study (PhD) in biostatistics, statistics, epidemiology or related quantitative disciplines, particularly if they have completed a research thesis.

Why study at CUNY (The City University of New York)

CUNY’s graduate programmes offer access to a large, diverse urban research ecosystem in New York City, which is rich in hospitals, public-health agencies, academic medical centres and biotech firms. Students benefit from applied research collaborations, internship opportunities and multidisciplinary faculty expertise across CUNY campuses.

• Practical connections to public-health agencies, hospital research units and industry partners located in the city, facilitating practicum and internship placements.
• Faculty with applied and methodological research interests in areas such as clinical trials, epidemiology, environmental health statistics and health-data science.
• A supportive environment for students from diverse backgrounds, with professional-development resources, statistical consulting services and career-placement support across the university system.