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Training Students to Extract Value from Big Data Summary of a Workshop (2015) / Chapter Skim
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1 Introduction
Pages 1-7

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
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From page 1...
... . Big data has become pervasive because of the availability of high-throughput data collection technologies, such as information-sensing mobile devices, remote sensing, radiofrequency identification readers, Internet log records, and wireless sensor networks.
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From page 2...
... To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and char acteristics 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. The topic of training students in big data is timely, as universities are already experimenting with courses and programs tailored to the needs of students who will work with big data.
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From page 3...
... CATS, which initiated the workshop, has tended to use the term massive data in the past, which implies data on a scale for which standard tools are not adequate. The terms data ­analytics and data science are also becoming common.
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From page 4...
... NATIONAL EFFORTS IN BIG DATA Suzanne Iacono, National Science Foundation Suzanne Iacono, of NSF, set the stage for the workshop by speaking about n ­ ational efforts in big data, current challenges, and NSF's motivations for spon soring the workshop. She explained that the workshop was an outgrowth of the national big data research and development (R&D)
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From page 5...
... A 2013 White House memorandum directed executive branch agencies to develop plans to increase public access to the results of federally funded research, including access to publications and data, and plans are under way at the agency level to address this memorandum. Iacono noted that increased access to publications is not difficult, because existing publication-access methods in professional societies and some government agencies can be used as models.
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From page 6...
... Seventeen agencies are involved in the Big Data Senior Steering Group, and each is implementing programs of its own related to big data. For example, DARPA has implemented three new p ­ rograms -- Big Mechanism, Memex, and Big Data Capstone; the National Insti tute of Standards and Technology maintains a Big Data Working Group; DOE has an Extreme Scale Science initiative; and NSF and NIH each has a broad portfolio related to big data.
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From page 7...
... Such companies as D ­ ataKind and Pivotal are matching data scientists with data problems in the nonprofit community. Universities, such as the University of Chicago, as discussed by Rayid Ghani (see Chapter 2)
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Key Terms

  • data science
  • extract value
  • training students
  • data research
  • massive data

  • data scientists
  • national big
  • data requires
  • iacono noted
  • term big

  • terms data
  • matching data
  • data scientist
  • chief data
  • connect data

  • data heterogeneity
  • data suzanne
  • regulating data
  • data senior
  • data analytics

  • labeled data
  • differing data
  • data depends
  • scant data
  • prospective data

  • data users
  • data capstone
  • solve big
  • using big
  • implies data


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