Courses & Curriculum

predicting poverty visualization


Predicting poverty and wealth from mobile phone metadata. Researchers: Joshua Blumenstock (University of Washington), Gabriel Cadamuro (University of Washington) and Robert On (UC Berkeley)

Degree Requirements

To earn your Master of Science in Data Science degree, you must complete 45 credits, consisting of nine core courses (40 credits), and a final, two-quarter group capstone project (5 credits). 

Curriculum & Course Sequence

The Master of Science in Data Science curriculum is designed to provide the breadth and depth of knowledge needed for a successful career in data science. It emphasizes practical proficiency in applying the relevant skills through courses in statistics, machine learning, human-centered data science and visualization, scalable data systems and data management. Depending on the course, students can expect an emphasis on Python and R programming, and some assignment work with Java.

Students can choose to take one or two courses per quarter, attending class either one or two evenings per week. Courses are expected to be offered in the following order:

Students generally progress through this sequence of required courses as a cohort. Master of Science in Data Science courses are not open for single-course enrollment, and no seats are available to non-matriculated students.

A Team-Based Learning Approach

Many of the courses will emphasize team-based data analysis and engineering work and will involve working in small groups to complete one or more guided practicum projects per quarter. The final course is a capstone project where students get to solve a real-world data analysis challenge facing a local organization.