Data Science at Seattle University

Our data science curriculum is designed in collaboration with industry professionals, equipping you with the most sought-after technical and professional skills in today's job market. Our graduates thrive in Seattle's dynamic tech landscape and are employed by some of the region’s most influential companies, including Amazon, Boeing, Fred Hutchinson Cancer Research Center, Meta, Microsoft, Google, Starbucks and others.

Our graduate data science programs are designed for working professionals who want to advance their skills without putting their careers on hold. With full- and part-time enrollment options, you can choose a pace that fits your goals and schedule while gaining the technical and professional skills to move your career forward.

Not quite ready to commit to a master's degree? Start with the 9-credit Graduate Certificate in Data Science, which can be completed in just one academic year. Upon successful completion, you can continue your studies and enroll directly in the MS in Data Science, applying all certificate credits toward the master's degree. That means you can build your credentials in stages without repeating coursework.

Our data science programs prioritize hands-on learning and practical application, culminating in an industry-aligned capstone project. Through our renowned Project Center, you'll work in small teams to complete a data analysis project from beginning to end that addresses a real-world challenge in the field of data science. From defining the problem to delivering actionable insights, you'll apply everything you've learned to create data-driven solutions that make an impact.

A graduate degree or certificate from Seattle University's College of Science and Engineering is an opportunity to lead positive change in the field of data and information sciences. Rooted in our mission to empower leaders for a just and humane world, our programs go beyond technical training and examine the ethical and legal aspects of technology. Explore topics such as data dignity, data set bias, technology's compatibility with social justice values, ethical implementation and more. You'll be prepared to drive innovation responsibly and equitably.