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The real work of data science : = tu...

Redman, Thomas C.

The real work of data science : = turning data into information, better decisions, and stronger organizations /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The real work of data science :/ Ron S. Kenett, Thomas C. Redman.
Reminder of title:	turning data into information, better decisions, and stronger organizations /
Author:	Kenett, Ron.
other author:	Redman, Thomas C.
Published:	Hoboken, NJ :Wiley, : c2019.,
Description:	xix, 114 p. :ill. ; : 25 cm.;
Subject:	Knowledge management. -
ISBN:	9781119570707 (pbk.) :

The real work of data science : = turning data into information, better decisions, and stronger organizations /
Kenett, Ron.

The real work of data science :turning data into information, better decisions, and stronger organizations /Ron S. Kenett, Thomas C. Redman. - Hoboken, NJ :Wiley,c2019. - xix, 114 p. :ill. ;25 cm.

Includes bibliographical references (p. [101]-106) and index.

A higher calling -- The difference between a good data scientist and a great one -- Learn the business -- Understand the real problem -- Get out there -- Sorry, but you can't trust the data -- Make it easy for people to understand your insights -- "When the data leaves off and your intuition takes over -- Take accountability for results -- What does it mean to be 'data-driven' -- Rooting out bias in decision-making -- Teach, teach, teach -- Evaluating data science outputs more formally -- Educating senior leaders -- Putting data science, and data scientists, in the right spots -- Moving up the analytics maturity ladder -- The industrial revolutions and data science -- Epilogue -- Appendix A. Skills of the data scientist -- Appendix B. Data defined -- Appendix C. Questions to help evaluate the outputs of data science -- Appendix D. Ethical considerations and today's data scientist -- Appendix E. Recent technical advances in data science.

"The essential guide for data scientists and for leaders who must get more from their data science teams. The Economist boldly claims that data are now 'the world's most valuable resource.' But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. Individual data scientists must fully extend themselves. They must make sure they understand the real problems their companies and agencies face, they must build trust with decision-makers, deal with quality issues, help decision makers become more demanding customers of data science, and they must teach their colleagues how to understand and interpret data science--even conduct basic analyses themselves. Further up in the management chain, managers of data science teams must help senior leaders understand where data and data science fit, ensure their teams are placed in the right spots organizationally, and put in place programs that help the entire organization become data-driven. This Kenett and Redman claim, is the 'real work of data science.' And it is this work that will spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is 'the most valuable resource'"--

ISBN: 9781119570707 (pbk.) :NT1066

LCCN: 2019003271Subjects--Topical Terms:

558406
Knowledge management.

LC Class. No.: HD30.2 / .K4573 2019

Dewey Class. No.: 658.4/038

The real work of data science : = turning data into information, better decisions, and stronger organizations /
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100 1 $a Kenett, Ron. $3 1060201
245 1 4 $a The real work of data science : $b turning data into information, better decisions, and stronger organizations / $c Ron S. Kenett, Thomas C. Redman.
260 # $a Hoboken, NJ : $b Wiley, $c c2019.
300 $a xix, 114 p. : $b ill. ; $c 25 cm.
504 $a Includes bibliographical references (p. [101]-106) and index.
505 0 # $a A higher calling -- The difference between a good data scientist and a great one -- Learn the business -- Understand the real problem -- Get out there -- Sorry, but you can't trust the data -- Make it easy for people to understand your insights -- "When the data leaves off and your intuition takes over -- Take accountability for results -- What does it mean to be 'data-driven' -- Rooting out bias in decision-making -- Teach, teach, teach -- Evaluating data science outputs more formally -- Educating senior leaders -- Putting data science, and data scientists, in the right spots -- Moving up the analytics maturity ladder -- The industrial revolutions and data science -- Epilogue -- Appendix A. Skills of the data scientist -- Appendix B. Data defined -- Appendix C. Questions to help evaluate the outputs of data science -- Appendix D. Ethical considerations and today's data scientist -- Appendix E. Recent technical advances in data science.
520 # $a "The essential guide for data scientists and for leaders who must get more from their data science teams. The Economist boldly claims that data are now 'the world's most valuable resource.' But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. Individual data scientists must fully extend themselves. They must make sure they understand the real problems their companies and agencies face, they must build trust with decision-makers, deal with quality issues, help decision makers become more demanding customers of data science, and they must teach their colleagues how to understand and interpret data science--even conduct basic analyses themselves. Further up in the management chain, managers of data science teams must help senior leaders understand where data and data science fit, ensure their teams are placed in the right spots organizationally, and put in place programs that help the entire organization become data-driven. This Kenett and Redman claim, is the 'real work of data science.' And it is this work that will spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is 'the most valuable resource'"-- $c Provided by publisher.
650 # 0 $a Knowledge management. $3 558406
700 1 # $a Redman, Thomas C. $3 1312018