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007			cr |||||||||||
008			190422t20192019nju ob 001 0 eng
020			_a9781119570714 _qAdobe electronic book
020			_a1119570719 _qAdobe electronic book
020			_z9781119570769 _qelectronic publication
020			_z111957076X
020			_a9781119570790 _qelectronic book
020			_a1119570794 _qelectronic book
020			_z9781119570707 _qpaperback
029	1		_aAU@ _b000065220341
029	1		_aCHNEW _b001050886
029	1		_aCHVBK _b567422399
037			_aD037AC3B-F3E4-4991-AD7E-24D5357AB94B _bOverDrive, Inc. _nhttp://www.overdrive.com
040			_aDLC _beng _erda _epn _cDLC _dNST _dDG1 _dEBLCP _dRECBK _dMERER _dOCLCO _dUKAHL _dOCLCF _dTEFOD _dOCLCQ _dYDX _dBRX
042			_apcc
049			_aMAIN
090			_aHD30.2 _b.K4573 2019
100	1		_aKenett, Ron, _eauthor.
245	1	4	_aThe real work of data science : _bturning data into information, better decisions, and stronger organizations / _cRon S. Kenett, Thomas C. Redman.
250			_a[First] edition.
264		1	_aHoboken, NJ, USA : _bWiley, _c2019.
264		4	_c©2019
300			_a1 online resource
336			_atext _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
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''-- _cProvided by publisher.
504			_aIncludes bibliographical references and index.
505	0		_aA 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.
588	0		_aPrint version record and CIP data provided by publisher; resource not viewed.
650		0	_aKnowledge management.
655		0	_aElectronic books.
655		4	_aElectronic books.
700	1		_aRedman, Thomas C.
776	0	8	_iPrint version: _aKenett, Ron. _tReal work of data science. _dHoboken, NJ : Wiley, 2019 _z9781119570707 _w(DLC) 2019003271
856	4	0	_uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url= _uhttps://doi.org/10.1002/9781119570790 _zWiley Online Library
907			_a.b16817187 _b2022-10-31 _c2020-07-17
942			_n0 _kHD30.2 .K4573 2019
998			_ae _b2020-07-17 _cm _dz _feng _gnju _y0 _z.b16817187
999			_c649127 _d649127