University of San Diego

What you'll study

The programme combines foundations in mathematics and statistics with applied courses in computer science and domain-focused electives. Core topics typically include statistical inference and probability, machine learning and predictive modelling, data structures and algorithms, database systems, data engineering for big data, and data visualisation. Students also study ethical and legal considerations in data use, reproducible research practices, and computational methods such as numerical analysis and optimization.

• Core modules: probability and statistics for data science, machine learning, data management and databases, algorithms and data structures, applied linear algebra and optimisation.
• Applied modules: natural language processing, deep learning, time series analysis, computer vision, cloud computing and distributed data systems.
• Electives and domain options: business analytics, biomedical data analysis, geospatial analytics, or social data science, allowing interdisciplinary application of technical skills.
• Capstone project or practicum: a substantial hands-on project, often completed with an industry partner or as a research-led thesis, emphasising end-to-end data workflows from problem framing and data collection to deployment and evaluation.

Teaching methods include lectures, programming labs, project-based courses and seminars. Students work with common industry tools and languages (for example Python, R, SQL, and cloud platforms) and gain experience with version control, containerisation and unit testing to support reproducible, production-ready solutions.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree from an accredited institution. Degrees in computer science, engineering, mathematics, statistics, physics or other quantitatively oriented disciplines are typical, though applicants from other backgrounds with strong quantitative preparation are considered.

• Academic preparation: undergraduate coursework in calculus, linear algebra, probability and statistics, and some programming experience are usually required or expected.
• Application materials: a completed application form, official transcripts, a statement of purpose outlining academic and career objectives, and a current résumé or CV.
• References and assessment: one or more academic or professional references are normally requested. Admissions may consider work experience in lieu of specific academic prerequisites where appropriate.
• English language proficiency: applicants whose first language is not English will be required to demonstrate proficiency in English in line with university policy.

Admissions may offer conditional entry or suggest preparatory coursework for applicants who are strong in other areas but lack specific prerequisites in programming or mathematics.

Career prospects

Graduates from the master’s in data science typically move into roles that require advanced analytic and computational skills. Common job titles include data scientist, machine learning engineer, data analyst, data engineer, business intelligence analyst, and research scientist. Graduates also find opportunities in specialised domains such as healthcare analytics, finance and risk modelling, autonomous systems, and public policy analytics.

The programme prepares students for employment in industry, technology startups, consulting firms, healthcare organisations and government agencies, and for further study such as doctoral research in computational and data science disciplines. Practical experience gained through capstone projects and industry collaborations often supports direct entry into professional roles or internships.

Why study at University of San Diego

The University of San Diego offers a focused, small-cohort environment with personalised attention from faculty who combine academic research and applied experience. The campus’s proximity to a vibrant regional technology and biotech sector provides access to local employers for internships, projects and networking. USD emphasises experiential learning, interdisciplinary collaboration across engineering, business and health sciences, and ethical leadership in technology—aligning technical training with real-world impact.

Students benefit from modern computing resources, opportunities for collaborative research, and career services that support placement and professional development. The programme’s structure is geared to equip graduates with both the theoretical understanding and the hands-on skills needed to translate data into actionable solutions.