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Suggested Citation:"Appendix B: Workshop Agenda." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
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B

Workshop Agenda

APRIL 11, 2014

 
8:30 a.m. Opening Remarks
 
Suzanne Iacono, Deputy Assistant Director, Directorate for Computer and Information Science and Engineering, National Science Foundation
 
8:40 The Need for Training: Experiences and Case Studies
 
Co-Chairs: Raghu Ramakrishnan, Microsoft Corporation John Lafferty, University of Chicago
Speakers: Rayid Ghani, University of Chicago Guy Lebanon, Amazon Corporation
 
10:15 Principles for Working with Big Data
 
Chair: Brian Caffo, Johns Hopkins University
Speakers: Jeffrey Ullman, Stanford University Alexander Gray, Skytree Corporation Duncan Temple Lang, University of California, Davis Juliana Freire, New York University
 
Suggested Citation:"Appendix B: Workshop Agenda." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
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12:45 p.m. Lunch
 
1:45 Courses, Curricula, and Interdisciplinary Programs
 
Chair: James Frew, University of California, Santa Barbara
Speakers: William Howe, University of Washington Peter Fox, Rensselaer Polytechnic Institute Joshua Bloom, University of California, Berkeley
 
4:30 Q&A/Discussion
 

APRIL 12, 2014

 
8:30 a.m. Shared Resources
 
Chair: Deepak Agarwal, LinkedIn Corporation
Speakers: Christopher Ré, Stanford University Bill Cleveland, Purdue University Ron Brachman, Yahoo Labs Mark Ryland, Amazon Corporation
 
11:15 Panel Discussion: Workshop Lessons
 
Chair: Robert Kass, Carnegie Mellon University
Panel Members: James Frew, University of California, Santa Barbara Deepak Agarwal, LinkedIn Corporation Claudia Perlich, Dstillery Raghu Ramakrishnan, Microsoft Corporation John Lafferty, University of Chicago
 
1:00 p.m. Workshop Adjourns
Suggested Citation:"Appendix B: Workshop Agenda." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
×
Page 52
Suggested Citation:"Appendix B: Workshop Agenda." National Research Council. 2015. Training Students to Extract Value from Big Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/18981.
×
Page 53
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As the availability of high-throughput data-collection technologies, such as information-sensing mobile devices, remote sensing, internet log records, and wireless sensor networks has grown, science, engineering, and business have rapidly transitioned from striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human's ability to examine, let alone absorb, it. Data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats.

The nation's ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program.

Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council's Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula.

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