Dartmouth College

What you'll study

The Applied Statistics curriculum emphasises core statistical theory, data analysis, and computational tools. You study probability, statistical inference, regression and multivariate methods, and design of experiments, alongside coursework in programming and data management. Typical modules include introductory probability and mathematical statistics, linear models and regression, statistical computing with R and Python, time series and forecasting, Bayesian methods, causal inference and survey sampling.

The programme is structured to balance theory and application. Early terms focus on calculus and probability foundations before moving to intermediate and advanced statistical methods. Applied labs and project courses give hands‑on experience with real datasets; students often take electives in machine learning, data visualisation, database systems and domain areas such as biology, economics, environmental science or public health to build interdisciplinary expertise.

Students are encouraged to undertake a capstone project or senior thesis involving data collection, analysis and written communication of results. There are opportunities for supervised undergraduate research with faculty, collaborative projects with departments across the College, and internships that integrate statistical practice with domain knowledge.

Entry requirements

Dartmouth looks for strong academic achievement across a broad secondary‑school curriculum. Successful applicants typically have a solid background in mathematics (including calculus) and demonstrate quantitative maturity; courses in statistics, computer science or advanced mathematics are an advantage. Practical experience with programming or data analysis (for example in R, Python or similar) strengthens an application.

Admission to Dartmouth is competitive and assessed holistically. Typical expectations include a high school record showing rigorous coursework, teacher recommendations, and evidence of intellectual curiosity and extracurricular engagement. Standardised test submission policies vary; consult Dartmouth’s admissions office for current guidance on testing and recommended supporting materials. Transfer applicants should show comparable college‑level mathematics and statistics preparation and submit transcripts for review.

Career prospects

Graduates with a degree in Applied Statistics pursue careers across industry, government, healthcare and research. Common roles include data analyst, data scientist, biostatistician, quantitative analyst in finance, policy analyst, market researcher and roles in product analytics or operations research. Employers range from tech companies and financial institutions to healthcare organisations, research labs and consulting firms.

Many alumni also progress to graduate study in statistics, biostatistics, data science, computer science, economics or related fields, or pursue professional training in law, business or medicine. The programme’s emphasis on computation and communication prepares graduates to translate statistical results for technical and non‑technical audiences—a key skill in interdisciplinary teams.

Why study at Dartmouth College

Dartmouth offers a liberal‑arts environment with small class sizes and close faculty mentoring, allowing personalised guidance through the quantitative curriculum. The College’s strength in undergraduate teaching means students have frequent access to faculty for research supervision, independent study and academic advising.

Students benefit from cross‑departmental collaboration with mathematics, computer science, engineering and domain departments, as well as experiential learning through internships, on‑campus research projects and off‑campus practicum placements. Dartmouth’s location in Hanover provides a tight‑knit academic community and strong alumni networks that help with internship and career opportunities.

Overall, the Applied Statistics programme at Dartmouth prepares students both for immediate employment in data‑driven roles and for advanced study, combining rigorous statistical training with practical computational skills and interdisciplinary application.