Dartmouth University

What you'll study

The programme combines foundational theory with applied coursework and a substantial project component. Core modules typically cover probability and statistical inference, supervised and unsupervised machine learning, regression and time-series modelling, experimental design and causal inference, and optimisation for analytics. Students also take practical units in data engineering and software for data science, such as databases, data pipelines, cloud-based computing, and reproducible analysis.

Elective options allow specialisation in areas such as natural language processing, deep learning, spatial and geostatistics, Bayesian methods, business analytics, or privacy and ethics in data. Teaching methods include lectures, hands-on labs, case studies and group work. A capstone project or practicum with an external partner is a central element: teams apply end-to-end analytics — from data ingestion and cleaning to model development, validation and deployment — on a real dataset, presenting findings to faculty and stakeholders.

Typical modules

• Probability and Statistical Inference
• Machine Learning and Predictive Modelling
• Data Engineering and Scalable Computing
• Time Series and Longitudinal Data Analysis
• Experimental Design and Causal Inference
• Data Visualisation and Communication
• Ethics, Privacy and Responsible Data Use
• Capstone Project / Industry Practicum

Entry requirements

Applicants are expected to have a bachelor’s degree from a recognised institution, preferably in a quantitative discipline such as mathematics, statistics, computer science, engineering, physics, economics or a related field. Typical academic prerequisites include undergraduate coursework in calculus, linear algebra, probability/statistics and programming. Demonstrable programming experience (for example in Python, R or SQL) is normally required.

Admission usually requires a curriculum vitae, academic transcripts, a personal statement outlining your interest in data analytics and career goals, and two academic or professional references. Some applicants may be asked to provide a sample of analytical work or complete an online skills assessment. International applicants should demonstrate English language proficiency where applicable. The programme may consider relevant professional experience in place of some formal prerequisites on a case‑by‑case basis.

Career prospects

Graduates pursue a wide range of roles across sectors. Common job titles include data scientist, data analyst, machine learning engineer, business intelligence analyst, data engineer, quantitative analyst and analytics consultant. Alumni work in technology companies, finance, healthcare, consulting firms, government agencies, and research organisations.

The programme’s emphasis on applied projects and teamwork prepares students for roles that require translating analytical results into business or policy recommendations. Graduates may also continue to doctoral study in statistics, computer science or related fields if they are inclined toward research careers.

Why study at Dartmouth University

Dartmouth University offers a small cohort environment that emphasises close mentoring by faculty, opportunities for interdisciplinary collaboration and hands‑on learning. Students benefit from access to research-active academics across statistics, computer science and domain areas, enabling tailored supervision for capstone and research projects.

The institution’s links with industry and local organisations provide practical practicum options and internship pathways, while its career services and alumni network support professional development and job placement. The programme’s balance of theory, engineering and communication prepares graduates to take on analytic roles where technical skill and clear storytelling are both highly valued.