[School-of-data] Training Students to Extract Value from Big Data: Summary of a Workshop (2014)

Lucy Chambers lucy.chambers at okfn.org
Mon Oct 20 09:37:43 UTC 2014

Thanks for sharing, Simon! Will take a look :)

On 15 October 2014 01:05, Simon Cropper <simoncropper at fossworkflowguides.com
> wrote:

> Hi,
> I thought others on this list may be interested in this publication.
> TITLE: Training Students to Extract Value from Big Data: Summary of a
> Workshop (2014).
> FREE PDF (http://www.nap.edu/catalog.php?record_id=18981&utm_
> source=NAP+Newsletter&utm_campaign=a7d6565192-NAP_mail_
> new_2014_10_14&utm_medium=email&utm_term=0_96101de015-
> a7d6565192-102708385&mc_cid=a7d6565192&mc_eid=0e0c2adb52)
> 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.
>                                   The National Academies Press 2014
> --
> Cheers Simon
>    Simon Cropper - Open Content Creator
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*Lucy Chambers*

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