Marymount University

What you'll study

The programme combines core statistical theory and methods with applied training in biological and health contexts. You will study probability, mathematical statistics, regression and multivariate analysis alongside computational courses that cover statistical programming and data management. Applied biostatistics modules focus on survival analysis, clinical trials methodology, epidemiologic methods, and the analysis of longitudinal and categorical data.

• Core statistics: probability, mathematical statistics, linear models, regression diagnostics.
• Computing and data science: statistical programming (R and/or Python), data visualisation, database fundamentals, reproducible research.
• Biostatistics and health methods: epidemiology, design and analysis of clinical trials, survival analysis, longitudinal data analysis, categorical data methods.
• Supporting sciences: introductory biology, human physiology for statisticians, public health concepts, research ethics.
• Capstone and research: a senior project or practicum emphasising real‑world data, often undertaken in collaboration with faculty, local hospitals, public‑health agencies or industry partners.

The degree typically includes general education requirements and electives that allow you to tailor the programme toward molecular biology, public health, or computational data science. Laboratory classes, internships and undergraduate research are commonly available to give practical experience with health data and study design.

Entry requirements

Marymount looks for students with a strong quantitative background and readiness for college‑level mathematics. Typical expectations include:

• A completed secondary‑school qualification or equivalent with solid achievement in mathematics (algebra and precalculus/calculus are recommended).
• Transcripts demonstrating good overall academic performance; competitive applicants often present higher‑level maths when available.
• English language proficiency for non‑native speakers as shown by recognised tests or equivalent qualifications.
• For transfer applicants, a record of college coursework in calculus and introductory statistics is advantageous.

Marymount operates a holistic admissions process; additional materials such as a personal statement, recommendation letters, and evidence of quantitative coursework, extracurricular engagement in science or research, or relevant work experience strengthen an application. Standardised test submission may be optional depending on current institutional policy.

Career prospects

Graduates are prepared for roles applying statistical methods in health and biological settings as well as for continued study. Common early career positions include:

• Biostatistician or statistical analyst in hospitals, pharmaceutical companies and contract research organisations (entry‑level roles often involve data cleaning, descriptive analysis, and producing study reports).
• Clinical trials data coordinator or analyst supporting trial design, data management and regulatory reporting.
• Public‑health data analyst working with local or state health departments, NGOs and international health organisations.
• Research assistant positions in academic or government laboratories conducting epidemiologic or biomedical research.

Many graduates also pursue postgraduate study such as a master’s in biostatistics, epidemiology, public health (MPH) or data science, or professional paths in clinical research, regulatory affairs and health technology. The combination of statistical and domain knowledge makes alumni competitive for roles where data drives health decisions.

Why study at Marymount University

Marymount offers a personalised undergraduate experience with small class sizes and close faculty mentoring, which is beneficial for developing quantitative skills and completing a substantive senior project. The university’s location near Washington, D.C. provides access to a broad network of hospitals, public‑health agencies and research institutions for internships and collaborative projects.

Students benefit from applied learning opportunities, undergraduate research with faculty, and career services that facilitate internships and employer connections in the region. Interdisciplinary pathways with biology, nursing, and health sciences allow students to pair statistical training with domain expertise, preparing graduates for applied roles in medicine, public health and the pharmaceutical sector.