Dartmouth University

What you'll study

The programme combines core training in programming, mathematics and statistics with applied modules that emphasise real-world datasets and project work. Early years focus on foundational topics such as introduction to programming, data structures, calculus, linear algebra and probability. Intermediate and advanced modules typically cover statistical inference, regression modelling, databases and SQL, data visualisation, machine learning, time series and experimental design.

• Core modules: programming for data analysis, probability & statistics, data management and databases, linear algebra.
• Applied modules: data visualisation, supervised and unsupervised learning, natural language processing, big-data architectures and cloud-based analytics.
• Practice and integration: capstone project or honours thesis, supervised research projects, team-based data challenges and coursework emphasising reproducible analysis.
• Complementary study: electives from computer science, statistics, economics, social sciences or domain areas such as healthcare and environmental science to provide context for data applications.

Laboratory classes and programming labs are embedded throughout the course, with emphasis on tools commonly used in industry (for example Python, R and SQL) and on communicating findings to non-technical audiences. Internships and industry projects are encouraged to develop workplace experience.

Entry requirements

Applicants should demonstrate strong quantitative ability and an aptitude for computing. Typical academic preparation includes substantive study in mathematics; additional credentials in statistics, computer science or related subjects are advantageous.

• Academic qualifications: high school qualifications equivalent to strong A-levels or IB scores, with mathematics at a high level.
• Recommended subjects: mathematics and either computer science, statistics or a science subject that demonstrates numerical competence.
• Supporting materials: personal statement outlining interest in data analytics, and at least one academic reference. Applicants with practical coding experience or evidence of project work should highlight this in their application.
• Other considerations: some applicants may be invited for interviews or to provide examples of previous analytical or coding work; mature applicants and those with non-traditional backgrounds are considered on an individual basis.

Career prospects

Graduates are well prepared for a range of analytic and technical roles across sectors. Typical entry-level job titles include data analyst, business intelligence analyst, junior data scientist, analytics consultant and database developer. With additional experience or postgraduate study, graduates move into roles such as senior data scientist, machine learning engineer, analytics manager or specialised domain analyst in finance, healthcare, government, technology and consulting.

The programme also provides a strong foundation for further study at master’s or doctoral level in data science, statistics, computer science or applied fields such as econometrics and biostatistics.

Why study at Dartmouth University

The programme benefits from an interdisciplinary environment that brings together expertise in computer science, statistics and domain departments. Smaller class sizes allow close faculty supervision on projects and research placements. Students gain hands-on experience through labs, capstone projects and partnerships with local industry and research centres, and can draw on wider campus resources — including business and engineering collaborations — to shape applied data work.

Students at Dartmouth also benefit from a liberal-arts approach that emphasises clear communication, ethics and critical thinking alongside technical skill, preparing graduates to apply analytics responsibly across a wide range of real-world contexts.