Master of Science in Data Science

The master of science (MS) in data science at the University of Oregon prepares students to effectively use data to make positive change in the world. To do this, a data scientist must engage with the bigger picture, from data sourcing and curation through goal formulation to outcome evaluation and communication.

Our program equips students with both expertise in cutting edge data science methodology, and the broader competencies needed to work across diverse teams and real-world applications. These enduring skills help graduates achieve long-term career goals and thrive as effective data scientists in any setting.

The MS in data science enables students from a wide variety of academic and professional backgrounds to achieve mastery of data science techniques in as little as one year. Students in the program work with real-world data to formulate and implement models and analyses, gain experience with modern predictive and inferential toolsets, communicate complex analyses intuitively to stakeholders, and skillfully navigate social and ethical dimensions of data-based decision-making.

Whether you're bound for a career as a data analyst at a Fortune 500, doing fundamental science, or informing local decision-making, we have room in our program for your background, interests, and goals.

Graduate Admissions

Degree Overview

The MS degree in data science requires 45 credits of coursework. It is a course-based program with a substantial amount of research/internship work:

• Core courses: 24 credits, must earn a grade of B- or higher
• Elective courses: 12 credits, must earn a grade of C or higher
• Research/internship: 9 credits, from research, internship, and capstone options

Note that classes cannot be used to meet multiple requirements. For example, a class taken from the "core" courses cannot also count as an elective course.

Domain focus: We expect students to choose a domain area of data science to focus on. This will guide choices of electives and the focus of capstone research. Upon entering the program, students will meet with an advisor to discuss this area and choose a class schedule and research plan that matches that area of interest.

Graduation timeline: The program can be completed in one year if all prerequisites are satisfied, but it may take additional time if additional courses are needed. Here is an example one-year (three term) schedule:

Sample Study Plan

Sample two-year (six term) schedule for a student who requires foundational coursework in mathematics and statistics:

Year 1

Year 2

It is also possible to finish in four or five terms. Longer time periods have substantially more time for in-depth data science project engagement.

Prerequisites

Incoming students must have experience in programming, statistics, and math, or be prepared to take fast-paced introductory courses in these. Successful students should have experience in at least one of these three areas, and a strong connection to data. Students aiming to finish in one year must have experience in all three areas by the time they start the program. More details are provided in the admissions process.

Capstone Project

Students in the program will complete an applied data science project, culminating in a presentation and report during the DSCI 636 class. This is not a thesis: projects will be scaffolded through the year, and expectations scaled to be appropriate for student workload. Students will have the option to bring their own data set, or to work with data from community/industry collaborators.