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
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
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
Nicholas 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 That Emerged Overnight
Full group discussion led by Andy Burnett, Knowinnovation
Identify the Most Promising Ideas and Possible Findings for the Committee’s Interim Report
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
WEBINAR
BUILDING DATA ACUMEN
September 12, 2017
Capstone Courses
Nicole Lazar, University of Georgia
NC State University Data Initiative
Mladen Vouk, North Carolina State University
Moderated Discussion
Tom Ewing, Virginia Tech
WEBINAR
INCORPORATING REAL-WORLD EXAMPLES
September 19, 2017
Using Urban and Sports Data in Student Projects
Cláudio Silva, New York University
Building a Talent Pipeline Through a Strategic Career Development Program and Academic-Industrial Partnership
Sears Merritt, MassMutual Financial Group
Moderated Discussion
Tom Ewing, Virginia Tech
WEBINAR
FACULTY TRAINING AND CURRICULUM DEVELOPMENT
September 26, 2017
Go to the People: Impactful Faculty Training in Data Science
Michael Posner, Villanova University
Shodor, National Computational Science Institute, XSEDE, and Blue Waters—How Can We Help?
Bob Panoff, Shodor Education Foundation
Moderated Discussion
Nicholas Horton, Amherst College
WEBINAR
COMMUNICATION SKILLS AND TEAMWORK
October 3, 2017
The Imperative of Interdisciplinarity in Data Science
Madeleine Clare Elish, Data and Society Research Institute
Data Science Collaboration for Public-Facing Research
Adam Hughes, Pew Research Center
Moderated Discussion
Lee Rainie, Pew Research
WEBINAR
INTERDEPARTMENTAL COLLABORATION AND
INSTITUTIONAL ORGANIZATION
October 10, 2017
Forging Virginia Tech’s Computational Modeling and Data Analytics (CMDA) Major Across Departments
Mark Embree, Virginia Tech
Some Thoughts on Data Science Education for Undergraduates
Mike Franklin, University of Chicago
Moderated discussion
Tom Ewing, Virginia Tech
WEBINAR
ETHICS
October 17, 2017
An Ethical Reasoning Framework for Data Science Education
Sorin Adam Matei, Purdue University
Ethical Thinking for Data Science Education
Brittany Fiore-Gartland, University of Washington
Moderated Discussion
Lee Rainie, Pew Research
WEBINAR
ASSESSMENT AND EVALUATION FOR
DATA SCIENCE PROGRAMS
October 24, 2017
Evaluation of Data Science Programs
Pamela Bishop, University of Tennessee, Knoxville
Assessing Data Science Learning Outcomes
Kari Jordan, Data Carpentry
Moderated Discussion
Louis Gross, University of Tennessee, Knoxville
WEBINAR
DIVERSITY, INCLUSION, AND INCREASING PARTICIPATION
November 7, 2017
Diversity, Inclusion, and Increasing Participation in Data Science
Allison Master, University of Washington
Diversity and Inclusion in Data Science: Using Data-Informed Decisions to Drive Student Success
Talithia Williams, Harvey Mudd College
Moderated Discussion
Nicholas Horton, Amherst College
WEBINAR
2-YEAR COLLEGES AND INSTITUTIONAL PARTNERSHIPS
November 14, 2017
Developing a 2-year College Certificate Program in Data Science
Brian Kotz, Montgomery College
Data Analytics Certificate Program at JCCC
Suzanne Smith, Johnson County Community College
Moderated Discussion
Laura Haas, University of Massachusetts Amherst
SECOND COMMITTEE MEETING
Washington, D.C.
December 6-7, 2017
Webinar Recaps
Tom Ewing, Virginia Tech
Nicholas Horton, Amherst College
Lee Rainie, Pew Research
Louis Gross, University of Tennessee, Knoxville
Laura Haas, University of Massachusetts Amherst
Big Data Hubs
Melissa Cragin, Midwest Big Data Hub
Renata Rawlings-Goss, South Big Data Hub
Comments from the National Science Foundation
Stephanie August, National Science Foundation
Chaitan Baru, National Science Foundation
