Seattle University

What you'll study

The programme blends core foundations in statistics, machine learning and computational methods with hands-on courses in data engineering, visualisation and domain-specific applications. Typical modules cover topics such as probability and statistical inference, supervised and unsupervised learning, deep learning, optimisation, databases and big-data systems, data visualisation, and reproducible data science workflows. Students also study computational mathematics relevant to modelling and simulation, and take coursework addressing data ethics, privacy and fairness.

Teaching is delivered through a mix of lectures, lab work and team projects. The curriculum culminates in a substantial capstone project or practicum in which students work with real-world data sets—often in collaboration with industry partners, research groups or nonprofit organisations—to deploy models, build pipelines and present findings to stakeholders.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree from an accredited institution. Strong candidates typically come from computer science, mathematics, statistics, engineering, physics, economics or related quantitative disciplines. Where an applicant’s background is less directly quantitative, the admissions committee looks for evidence of relevant coursework or experience in programming, calculus, linear algebra and introductory statistics.

Application materials usually include academic transcripts, a personal statement describing your goals and experience with data-related work, a current CV or résumé, and references. Some applicants may be asked to demonstrate programming ability (for example in Python or R) or complete preparatory coursework prior to starting the programme. International applicants must meet the university’s English language proficiency requirements.

Career prospects

Graduates enter a broad range of roles that require turning data into decisions. Typical job titles include data scientist, machine learning engineer, data engineer, business/analytics consultant, quantitative analyst and research analyst. Employers span technology firms, cloud and software vendors, healthcare systems, biotechnology companies, financial services, government agencies and civic organisations.

The programme’s emphasis on applied projects, reproducible pipelines and ethical use of data prepares students for roles that require both technical depth and the ability to communicate results to non-technical stakeholders. Alumni also use the degree as a springboard into doctoral study or research positions in computational science and engineering.

Why study at Seattle University

Seattle University combines rigorous technical training with a Jesuit-inspired focus on ethical practice and community engagement. Located in a major Pacific Northwest technology hub, the university offers proximity to employers ranging from large cloud and software companies to startups and research hospitals, helping students access internships, mentorship and applied projects.

Students benefit from small class sizes, close faculty mentoring and opportunities to work on interdisciplinary problems that connect computing, engineering and domain expertise. The programme emphasises hands-on learning with contemporary tools and platforms, and prepares graduates to apply data science responsibly in industry, government and the non-profit sector.