National Academies Press: OpenBook

Data Science for Undergraduates: Opportunities and Options (2018)

Chapter: Appendix B Meetings and Presentations

« Previous: Appendix A Biographies of the Committee
Suggested Citation:"Appendix B Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. doi: 10.17226/25104.
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Page 80
Suggested Citation:"Appendix B Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. doi: 10.17226/25104.
×
Page 81
Suggested Citation:"Appendix B Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. doi: 10.17226/25104.
×
Page 82
Suggested Citation:"Appendix B Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. doi: 10.17226/25104.
×
Page 83
Suggested Citation:"Appendix B Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. doi: 10.17226/25104.
×
Page 84
Suggested Citation:"Appendix B Meetings and Presentations." National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press. doi: 10.17226/25104.
×
Page 85

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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 B-1 PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION

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 B-2 PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION

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 B-3 PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION

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 B-4 PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION

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 THIRD COMMITTEE MEETING Washington, D.C. December 6-7, 2017 Webinar Recaps B-5 PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION

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 B-6 PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION

Next: Appendix C Contributing Individuals »
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Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent.

Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

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