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  • 1
    UID:
    kobvindex_INT58989
    Format: 1 online resource (139 pages)
    Edition: 1st ed.
    ISBN: 9781119570714
    Note: Intro -- Title Page -- Copyright Page -- Contents -- About the Authors -- Preface -- About the Companion Website -- Chapter 1 A Higher Calling -- The Life-Cycle View -- The Organizational Ecosystem -- Once Again, Our Goal -- Chapter 2 The Difference Between a Good Data Scientist and a Great One -- Chapter 3 Learn the Business -- The Annual Report -- SWOTs and Strategic Analysis -- The Balanced Scorecard and Key Performance Indicators -- The Data Lens -- Build Your Network -- Implications -- Chapter 4 Understand the Real Problem -- A Telling Example -- Understanding the Real Problem -- Implications -- Chapter 5 Get Out There -- Understand Context and Soft Data -- Identify Sources of Variability -- Selective Attention -- Memory Bias -- Implications -- Chapter 6 Sorry, but You Can't Trust the Data -- Most Data Is Untrustworthy -- Dealing with Immediate Issues -- Getting in Front of Tomorrow's Data Quality Issues -- Implications -- Chapter 7 Make It Easy for People to Understand Your Insights -- First, Get the Basics Right -- Presentations Get Passed Around -- The Best of the Best -- Implications -- Chapter 8 When the Data Leaves Off and Your Intuition Takes Over -- Modes of Generalization -- Implications -- Chapter 9 Take Accountability for Results -- Practical Statistical Efficiency -- Using Data Science to Perform Impact Analysis -- Implications -- Chapter 10 What It Means to Be "Data-driven" -- Data-driven Companies and People -- Traits of the Data-driven -- Traits of the Antis -- Implications -- Chapter 11 Root Out Bias in Decision-making -- Understand Why It Occurs -- Take Control on a Personal Level -- Solid Scientific Footings -- Implications -- Chapter 12 Teach, Teach, Teach -- The Rope Exercise -- The "Roll Your Own" Exercise -- The Starter Kit of Questions to Ask Data Scientists -- Implications , Chapter 13 Evaluating Data Science Outputs More Formally -- Assessing Information Quality -- A Hands-On Information Quality Workshop -- Implications -- Chapter 14 Educating Senior Leaders -- Covering the Waterfront -- Companies Need a Data and Data Science Strategy -- Organizations Are "Unfit for Data" -- Get Started with Data Quality -- Implications -- Chapter 15 Putting Data Science, and Data Scientists, in the Right Spots -- The Need for Senior Leadership -- Building a Network of Data Scientists -- Implications -- Chapter 16 Moving Up the Analytics Maturity Ladder -- Implications -- Chapter 17 The Industrial Revolutions and Data Science -- The First Industrial Revolution: From Craft to Repetitive Activity -- The Second Industrial Revolution: The Advent of the Factory -- The Third Industrial Revolution: Enter the Computer -- The Fourth Industrial Revolution: The Industry 4.0 Transformation -- Implications -- Chapter 18 Epilogue -- Strong Foundations -- A Bridge to the Future -- Appendix A Skills of a 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 -- References -- A List of Useful Links -- Videos, Blogs, and Presentations -- Articles -- Index -- EULA
    Additional Edition: Print version Kenett, Ron S. The Real Work of Data Science Newark : John Wiley & Sons, Incorporated,c2019 ISBN 9781119570707
    Language: English
    Keywords: Electronic books ; Electronic books
    URL: FULL  ((OIS Credentials Required))
    URL: FULL  ((OIS Credentials Required))
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    almahu_9948369494402882
    Format: 1 online resource (115 pages)
    ISBN: 9781119570714 (e-book)
    Additional Edition: Print version: Kenett, Ron. Real work of data science : turning data into information, better decisions, and stronger organizations. Hoboken, NJ : John Wiley & Sons, Inc., 2019 ISBN 9781119570707
    Language: English
    Keywords: Electronic books.
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    UID:
    almahu_9948198750102882
    Format: 1 online resource
    Edition: [First] edition.
    ISBN: 9781119570714 , 1119570719 , 9781119570790 , 1119570794
    Content: "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'"--
    Note: 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.
    Additional Edition: Print version: Kenett, Ron. Real work of data science. Hoboken, NJ : Wiley, 2019 ISBN 9781119570707
    Language: English
    Keywords: Electronic books. ; Electronic books.
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    UID:
    edocfu_9959327367402883
    Format: 1 online resource
    Edition: [First] edition.
    ISBN: 9781119570714 , 1119570719 , 9781119570769 , 111957076X , 9781119570790 , 1119570794
    Content: "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'"--
    Note: 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.
    Additional Edition: Print version: Kenett, Ron. Real work of data science. Hoboken, NJ : Wiley, 2019 ISBN 9781119570707
    Language: English
    Keywords: Electronic books.
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    UID:
    almafu_9959327367402883
    Format: 1 online resource
    Edition: [First] edition.
    ISBN: 9781119570714 , 1119570719 , 9781119570769 , 111957076X , 9781119570790 , 1119570794
    Content: "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'"--
    Note: 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.
    Additional Edition: Print version: Kenett, Ron. Real work of data science. Hoboken, NJ : Wiley, 2019 ISBN 9781119570707
    Language: English
    Keywords: Electronic books.
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
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