The world’s growing interest in data science is undeniable. One can see this reflected in the increased number of new academic degree and certificate programs, the number of new jobs available for individuals with data science skills, and the rising migration of researchers with computational skills to the private sector. In an effort to channel our efforts, we focus on six major themes around which academic data science discussions coalesce

Career Paths and Alternative Metrics

Working Group Lead: Tyler McCormick

Identify “data science fellows” who might otherwise slip through the cracks or go to industry, and groom them for a new breed of faculty position by creating new roles for data science professionals on campus that are not subject to “publish or perish,” and that are equipped to pursue pragmatic, high-impact software-oriented data science projects.

Learn more about Career Paths and Alternative Metrics

Education and Training

Working Group Leads: Bing Brunton & Steve Brunton

Establish alternative mechanisms that are free from departmental politics and conventional structures: boot camps, summer schools, tutorials.

Learn more about Education and Training

Software Tools, Environments, and Support

Working Group Lead: Jake Vanderplas

Successful projects are characterized by a balance between specialization and generality: sufficiently focused to actually solve a problem, but with the ability to scale to enough users or enough domains to amortize the cost of the initial development. We seek to institutionalize these patterns of success to help deliver the “next 100 Sloan Digital Sky Surveys.”

Learn more about Software Tools, Environments, and Support

Reproducibility and Open Science

Working Group Lead: Ariel Rokem

Establish a culture of reproducibility and open science, and develop tools to support an environment where researchers find both the tools and the best practices for their research to be openly accessible to society and fully reproducible, more effectively feeding a productive cycle of research.

Learn more about Reproducibility and Open Science

Members of the Data Science Studies Working Group meet

Data Science Studies

Working Group Leads: Anissa Tanweer & Cecilia Aragon

UW Data Science Studies is a group of cross-disciplinary researchers studying the sociocultural and organizational processes around the emerging practice of data science.

Learn more about Data Science Studies


Working Group Leads: Ariel Rokem

The neuroinformatics working group at the University of Washington (UW) eScience Institute and the University of Washington Institute for Neuroengineering (UWIN) focuses on neuroinformatics methods and their role in understanding the brain.

Learn more about Neuroinformatics

Neurohackweek participants work on their laptops

Algorithmic Foundations for Data Science Institute (ADSI) Working Group

Working Group Lead: John Thickstun

Seek new algorithms and design principles that unify ideas and provide a common language for addressing contemporary data science challenges at this working group.

Learn more about the ADSI Working Group

Working Spaces and Culture

Working Group Leads: Micaela Parker & Sarah Stone

Establish new physical spaces on our campuses, specifically designed to meet the new requirements of data science activities, which in many cases will flourish best outside of traditional departmental boundaries.

Learn more about Working Spaces and Culture