[School-of-data] school-of-data Digest, Vol 27, Issue 6

Mon Oct 20 18:38:22 UTC 2014

Thanks Lucy and Simon for sharing.

This document will be very appreciated by my students and the Montreal 
community

--- Médiation par | Curation by ---
Dre Diane Mercier
Ph.D. en sciences de l'information

@okfnca | ca.okfn.org
@MTL_DO | donnees.ville.montreal.qc.ca
@carnetsDM | dianemercier.com

Webographie du libre : 
https://www.zotero.org/dmercier/items/order/dateModified/sort/desc
« Pas de données ouvertes, sans logiciel libre ni formats ouverts »

Le 2014-10-20 08:00, school-of-data-request at lists.okfn.org a écrit :
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>     1. Re: Training Students to Extract Value from Big Data: Summary
>        of a Workshop (2014) (Lucy Chambers)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 20 Oct 2014 11:37:43 +0200
> From: Lucy Chambers <lucy.chambers at okfn.org>
> To: "Mailing list for the School of Data, a joint initiative of the
> 	OKFN and P2PU" <school-of-data at lists.okfn.org>
> Subject: Re: [School-of-data] Training Students to Extract Value from
> 	Big Data: Summary of a Workshop (2014)
> Message-ID:
> 	<CALQ2jXJvNak2SYVySjNGmDofpKEVomeW-Wd9MX8nph1Bgp5GNQ at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> 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)
>>
>> ABSTRACT
>>
>> 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
>>
>>     Free and Open Source Software Workflow Guides
>>     ------------------------------------------------------------
>>     Introduction               http://www.fossworkflowguides.com
>>     GIS Packages           http://www.fossworkflowguides.com/gis
>>     bash / Python    http://www.fossworkflowguides.com/scripting
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