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Suggested Citation:"Appendix B: Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
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B

Meetings and Presentations

FIRST COMMITTEE MEETING
Washington, D.C.
December 12–13, 2016

Lessons from Current Data Science Programs and Future Directions

Rebecca Nugent, Carnegie Mellon University
Rob Rutenbar, University of Illinois, Urbana-Champaign
David Culler, University of California, Berkeley
William Yslas Velez, University of Arizona
Duncan Temple Lang, University of California, Davis

Envisioning the Field of Data Science and Future Directions and Implications to Society

David Donoho, Stanford University
Lee Rainie, Pew Research Center

Expanding Diversity in Data Science—Among Student Populations and in Topic Areas Embraced by Data Science

Bhramar Mukherjee, University of Michigan
Deb Agarwal, Lawrence Berkeley National Laboratory
Andrew Zieffler, University of Minnesota

Questions that Should Be Asked to Envision the Future of Data Science for Undergraduates

Tom Ewing, Virginia Tech
Louis Gross, University of Tennessee, Knoxville
Chris Mentzel, Gordon and Betty Moore Foundation
Patrick Perry, New York University
John Abowd, U.S. Census Bureau

SECOND COMMITTEE MEETING
Webinar
April 25, 2017

Overview of the Study

Michelle Schwalbe, National Academies of Sciences, Engineering, and Medicine
Alfred Hero, University of Michigan
Laura Haas, IBM Almaden Research Center
Louis Gross, University of Tennessee, Knoxville

Suggested Citation:"Appendix B: Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×

Facilitated Discussion

Andy Burnett, Knowinnovation

WORKSHOP
Washington, D.C.
May 2-3, 2017

Opening Comments

Study Co-Chairs: Laura Haas, IBM, and Alfred Hero, III, University of Michigan

Comments from the National Science Foundation

Chaitan Baru, National Science Foundation

Overview of the Workshop

Andy Burnett, Knowinnovation

Workshop Themes

Skills and Knowledge for Future Data Scientists

Rob Rutenbar, University of Illinois, Urbana-Champaign

Broadening Participation in Data Science Education

Julia Lane, New York University

Future Delivery of Data Science Education

Nick Horton, Amherst College

Table Discussions About Key Questions

Question Exploration Groups

Small breakout groups to discuss all three questions

Feedback from Question Groups

Present ideas and discuss questions with full group

Integrate Ideas into Three Thematic Areas

Form three groups aligned with the thematic questions or possible new questions

Feedback from Question Groups

Share the integrated ideas with the full group

Plenary Discussion of Feedback

Study Co-Chairs: Laura Haas, IBM, and Alfred Hero, III, University of Michigan

New Questions and Ideas which Emerged Overnight

Full group discussion led by Andy Burnett, Knowinnovation

Identify the Most Promising Ideas and Possible Findings for the Committee’s Interim Report

Suggested Citation:"Appendix B: Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×

Small table groups

Backcast the Most Promising Ideas

Small table groups discuss what steps would have to be taken in order to implement the most promising ideas

Participants

Ani Adhikari—University of California, Berkeley

Stephanie August—National Science Foundation

Chaitan Baru—National Science Foundation

Quincy Brown—American Association for the Advancement of Science

Andy Burnett—Knowinnovation

Eva Campo—National Science Foundation

Linda Casola—National Academies of Sciences, Engineering, and Medicine

Alok Choudhary—Northwestern University

Catherine Cramer—New York Hall of Science

David Culler—University of California, Berkeley

Renee Dopplick—Association for Computing Machinery

Jon Eisenberg—National Academies of Sciences, Engineering, and Medicine

E. Thomas Ewing—Virginia Tech

William Finzer—Concord Consortium

Greg Goins—North Carolina A&T State University

Louis Gross—University of Tennessee, Knoxville

Laura Haas—IBM

Alfred Hero—University of Michigan

Nicholas Horton—Amherst College

Charles Isbell—Georgia Tech

Ryan Jones—Middle Tennessee State University

Nandini Kannan—National Science Foundation

Danny Kaplan—Macalester College

Brian Kotz—Montgomery College

Jay Labov—National Academies of Sciences, Engineering, and Medicine

Julia Lane—New York University

Sharon Lane-Getaz—St. Olaf College

Jeff Leek—Johns Hopkins University

Andrew McCallum—University of Massachusetts Amherst

Richard McCullough—Harvard University

Mary Kehoe Moynihan—Cape Cod Community College

Bhramar Mukherjee—University of Michigan

Claudia Neuhauser—University of Minnesota

Deborah Nolan—University of California, Berkeley

Rebecca Nugent—Carnegie Mellon University

Dennis Pearl—Pennsylvania State University

Gabriel Perez-Giz—National Science Foundation

Lee Rainie—Pew Research Center

Patrick Riley—Google

Andee Rubin—TERC

Rob Rutenbar—University of Illinois, Urbana-Champaign

Michelle Schwalbe—National Academies of Sciences, Engineering, and Medicine

Suggested Citation:"Appendix B: Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×

Amy Stephens—National Academies of Sciences, Engineering, and Medicine

Victoria Stodden—University of Illinois, Urbana-Champaign

Kristin Tolle—Microsoft

Ron Wasserstein—American Statistical Association

Ben Wender—National Academies of Sciences, Engineering, and Medicine

Elena Zheleva—National Science Foundation

Andrew Zieffler—University of Minnesota

Suggested Citation:"Appendix B: Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 49
Suggested Citation:"Appendix B: Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 50
Suggested Citation:"Appendix B: Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 51
Suggested Citation:"Appendix B: Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/24886.
×
Page 52
Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim Report Get This Book
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The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation’s ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses.

The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction.

This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education.

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