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

[Simon Cropper]

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

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