City University of Seattle

What you'll study

This master's programme covers the full data science lifecycle, from data acquisition and processing to modelling, deployment and communication. Study is typically a mix of core technical modules, electives that allow specialisation, and a culminating project or practicum that applies learning to a real dataset or organisational problem.

• Core topics: statistical inference and applied probability, machine learning and predictive modelling, data visualisation and exploratory analysis, data mining, and ethics and governance of data.
• Data infrastructure and engineering: database systems, data modelling, cloud data platforms, and big-data processing frameworks for scalable analytics.
• Programming and tools: coursework emphasises practical proficiency in languages and tools commonly used in industry (for example Python, R, SQL, and contemporary libraries for machine learning and data engineering).
• Electives and specialisms: students can usually choose electives in areas such as deep learning, natural language processing, time-series forecasting, business analytics, or domain-specific applications (healthcare, finance, IoT etc.).
• Capstone / practicum: a project-based capstone or practicum integrates technical skills with communication and project management, often using real-world data and supervised by faculty or industry partners.

Structure and study mode

The programme is designed with flexibility for full- or part-time study and often includes evening and online delivery options to accommodate working professionals. Assessment blends coursework, programming assignments, projects and the capstone.

Entry requirements

Applicants are expected to hold a bachelor's degree from a recognised institution. Relevant backgrounds include computer science, statistics, mathematics, engineering, or a quantitative discipline; applicants from other fields with demonstrable quantitative and programming skills are also considered.

• Academic qualifications: an undergraduate degree in a relevant subject or equivalent professional experience in analytical roles. Admissions decisions take the whole profile into account rather than a single metric.
• Skills and preparation: prior coursework or experience in programming (for example Python or R), calculus or linear algebra, and introductory statistics is normally required. Applicants who lack some prerequisites may be admitted conditionally and asked to complete foundation modules.
• Supporting materials: a personal statement, résumé/CV, and academic transcripts are typically required. References and examples of prior analytical work or a portfolio can strengthen an application.
• International applicants: proof of English language proficiency is required if previous education was not in English. Visa guidance and international student support are available through the university.

Career prospects

Graduates commonly move into roles that apply quantitative and computational skills to business and research problems. Typical job titles and career pathways include:

• Data Scientist — building predictive models, conducting experimental analysis and translating insights into business value.
• Machine Learning Engineer — productionising models, developing ML pipelines and integrating models into software products.
• Data Engineer — designing and maintaining data architectures, ETL pipelines and scalable data storage solutions.
• Business Intelligence or Analytics Specialist — delivering dashboards, reports and decision-support analytics for stakeholders.
• Applied Researcher or Domain Analyst — using data science methods in sectors such as healthcare, finance, retail, manufacturing or government.

The programme’s project emphasis and connections to the Seattle technology ecosystem help students develop practical portfolios and industry contacts that support recruitment into local and national employers.

Why study at City University of Seattle

City University of Seattle positions itself as a pragmatic, career-focused institution with flexible delivery designed for adults and professionals. Located in a major technology hub, the university offers opportunities to engage with industry through practicum projects, local employer networks and career services geared to placing graduates in analytics and engineering roles.

• Practical, applied approach: coursework is oriented toward hands-on skills, contemporary tools and real-world datasets to prepare graduates for immediate contribution in the workplace.
• Flexible formats: options for evening classes and online study make the programme suitable for those who are working or need part-time study.
• Small cohorts and faculty with industry experience: students benefit from close interaction with instructors who bring applied knowledge and professional networks.
• Career support: the university provides career services, resume and interview support, and opportunities to connect with employers in the Seattle region and beyond.

Overall, the MS in Data Science at City University of Seattle is aimed at learners seeking a pragmatic, industry-relevant education that builds technical depth, practical experience and career-ready skills in data science and engineering.