Education and Training

Working Group

Data science is inherently multi-disciplinary, requiring training in statistics, computer science, and one or more domain areas. While no one person may have all the requisite skills, as a community of data scientists, we need to encompass these broad skills and expertise as a set with sufficient cross-disciplinary fluency to enable us to communicate and collaborate effectively. Traditional university curricula, however, do not provide this kind of broad, cross-disciplinary data science training.

The goal of this working group is to learn from past training and education activities at our partner (and other) institutions to develop a new data science curricula that appeal to our heterogeneous data science community. We hope to overcome siloed approaches that waste resources and discourage interdisciplinary learning and discovery by developing instruction that is flexible yet keeps sight of a common core of skills, knowledge, and language.

Working with our partners at New York University and the University of Washington, we plan to develop effective bootcamp materials (covering many areas and skill levels) and will build on existing successful bootcamps, such as Software Carpentry, to extend their coverage to other data science topics. Additionally, we will offer tune-up camps and continuing-education activities. We also plan to develop several online courses in a variety of areas and for different skill levels as well as host hackathons and seminars.


Working Group Members

Joshua Bloom

Astronomy; Center for Time-Domain Informatics
Co-I for Moore/Sloan Data Science Environments

Philip Stark

Statistics; Statistical Computing Facility
Co-I for Moore/Sloan Data Science Environments

Jasjeet Sekhon

Political Science & Statistics
Co-I for Moore/Sloan Data Science Environments

Kathryn D. Huff

Nuclear Engineering
Data Science Fellow

Fatma Deniz

Helen Wills Neuroscience Institute, International Computer Science Institute

Nick Adams

Social Science
Research Fellow

Yu Feng

Berkeley Center for Cosmological Physics
Data Science Fellow

Justin Kitzes

Energy & Resources Group

Rachel Slaybaugh

Nuclear Engineering

Sandrine Dudoit

Biostatistics; Statistics

Carl Boettiger

Environmental Science, Policy, & Management

David Ackerly

Integrative Biology

Robert M. Nadeau

Berkeley Seismological Lab
Data Science Fellow

Kellie Ottoboni


Jasmine Nirody

Data Science Fellow

Garret Christensen

Berkeley Initiative for Transparency in the Social Sciences (BITSS)

Cyrus Dioun


David Culler

Electrical Engineering & Computer Sciences
Co-I for Moore/Sloan Data Science Environments

Cathryn Carson

Division of Social Sciences & History
Co-I for Moore/Sloan Data Science Environments

Anthony Suen

Data Science Education Program
Data Science Fellow