Program Details

The University of Washington Master of Science in Data Science is an evening program designed for professionals from diverse backgrounds who wish to pursue a career in data science. This new program offers a comprehensive, interdisciplinary curriculum, developed by leading faculty from six top-ranked UW departments and schools, in partnership with the eScience Institute:

Combining practically focused and technically rigorous training in statistical modeling, machine learning, software engineering, data management, data visualization and user interface design, the Master of Science in Data Science provides a powerful platform to advance in the data science field.

Program Format

This program is designed with working professionals in mind, with classes held in the evenings on the UW campus. You can elect to take the program on a full- or part-time basis. Full-time students take two courses per quarter, attending class two evenings a week to complete the program in one-and-a-half years. Part-time students take one course per quarter, attending class one evening a week to complete the degree in three years.

The curriculum consists of nine core courses, eight classroom-based courses and one capstone project. For more details, see the Courses & Curriculum page.

Who Should Apply

The Master of Science in Data Science is geared to professionals who may be employed full-time in technical or business positions.

Prospective students should have prior programming experience, mathematical training and an undergraduate degree in a science or engineering field. For complete admissions requirements, see the Admissions page.

Related Programs

If you’re not ready for a master’s program yet but are interested in learning more about data science, you might consider taking a related certificate program from UW Professional & Continuing Education.

The Certificate in Data Science focuses on teaching the computer science, mathematics and analytical skills needed to enter the field of data science. You will learn the fundamental tools and techniques used acquire valuable insights from data sets at any scale – from gigabytes to petabytes.