{"product_id":"the-real-work-of-data-science-isbn-9781119570707","title":"The Real Work of Data Science","description":"\u003cp\u003e\u003cb\u003eThe essential guide for data scientists and for leaders who must get more from their data science teams\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eThe Economist\u003c\/i\u003e 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. \u003ci\u003eThe Real Work of Data Science\u003c\/i\u003e explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that 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.\"\u003c\/p\u003e \u003cp\u003e\"These two authors are world-class experts on analytics, data management, and data quality; they've forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it.\"\u003cbr\u003e\u003cb\u003e—Thomas H. Davenport,\u003c\/b\u003e Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy\u003c\/p\u003e \u003cp\u003e\"I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today's issues, such as computational Big Data.\"\u003cbr\u003e\u003cb\u003e—Sir David Cox,\u003c\/b\u003e Warden of Nuffield College and Professor of Statistics, Oxford University\u003c\/p\u003e \u003cp\u003e\"Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers.\"\u003cb\u003e \u003cbr\u003e—A. Blanton Godfrey,\u003c\/b\u003e Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University\u003c\/p\u003e \u003cp\u003eAbout the Authors xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A Higher Calling 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Life‐Cycle View 2\u003c\/p\u003e \u003cp\u003e\u003ci\u003eProblem Elicitation: Understand the Problem \u003c\/i\u003e3\u003c\/p\u003e \u003cp\u003e\u003ci\u003eGoal Formulation: Clarify the Short‐term and Long‐term Goals \u003c\/i\u003e3\u003c\/p\u003e \u003cp\u003e\u003ci\u003eData Collection: Identify Relevant Data Sources and Collect the Data \u003c\/i\u003e3\u003c\/p\u003e \u003cp\u003e\u003ci\u003eData Analysis: Use Descriptive, Explanatory, and Predictive Methods \u003c\/i\u003e3\u003c\/p\u003e \u003cp\u003e\u003ci\u003eFormulation of Findings: State Results and Recommendations \u003c\/i\u003e4\u003c\/p\u003e \u003cp\u003e\u003ci\u003eOperationalization of Findings: Suggest Who, What, When, and How \u003c\/i\u003e5\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCommunication of Findings: Communicate Findings, Decisions, and Their Implications to Stakeholders \u003c\/i\u003e5\u003c\/p\u003e \u003cp\u003e\u003ci\u003eImpact Assessment: Plan and Deploy an Assessment Strategy \u003c\/i\u003e5\u003c\/p\u003e \u003cp\u003eThe Organizational Ecosystem 6\u003c\/p\u003e \u003cp\u003e\u003ci\u003eOrganizational Structure \u003c\/i\u003e6\u003c\/p\u003e \u003cp\u003e\u003ci\u003eOrganizational Maturity \u003c\/i\u003e6\u003c\/p\u003e \u003cp\u003eOnce Again, Our Goal 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Difference Between a Good Data Scientist and a Great One 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eImplications 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Learn the Business 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Annual Report 13\u003c\/p\u003e \u003cp\u003eSWOTs and Strategic Analysis 13\u003c\/p\u003e \u003cp\u003eThe Balanced Scorecard and Key Performance Indicators 14\u003c\/p\u003e \u003cp\u003eThe Data Lens 15\u003c\/p\u003e \u003cp\u003eBuild Your Network 16\u003c\/p\u003e \u003cp\u003eImplications 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Understand the Real Problem 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Telling Example 17\u003c\/p\u003e \u003cp\u003eUnderstanding the Real Problem 18\u003c\/p\u003e \u003cp\u003eImplications 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Get Out There 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstand Context and Soft Data 21\u003c\/p\u003e \u003cp\u003eIdentify Sources of Variability 22\u003c\/p\u003e \u003cp\u003eSelective Attention 23\u003c\/p\u003e \u003cp\u003eMemory Bias 23\u003c\/p\u003e \u003cp\u003eImplications 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Sorry, but You Can’t Trust the Data 25\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMost Data Is Untrustworthy 25\u003c\/p\u003e \u003cp\u003eDealing with Immediate Issues 27\u003c\/p\u003e \u003cp\u003eGetting in Front of Tomorrow’s Data Quality Issues 29\u003c\/p\u003e \u003cp\u003eImplications 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Make It Easy for People to Understand Your Insights 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFirst, Get the Basics Right 31\u003c\/p\u003e \u003cp\u003ePresentations Get Passed Around 33\u003c\/p\u003e \u003cp\u003eThe Best of the Best 34\u003c\/p\u003e \u003cp\u003eImplications 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 When the Data Leaves Off and Your Intuition Takes Over 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModes of Generalization 36\u003c\/p\u003e \u003cp\u003eImplications 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Take Accountability for Results 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePractical Statistical Efficiency 39\u003c\/p\u003e \u003cp\u003eUsing Data Science to Perform Impact Analysis 41\u003c\/p\u003e \u003cp\u003eImplications 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 What It Means to Be “Data‐driven” 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData‐driven Companies and People 43\u003c\/p\u003e \u003cp\u003eTraits of the Data‐driven 44\u003c\/p\u003e \u003cp\u003eTraits of the Antis 46\u003c\/p\u003e \u003cp\u003eImplications 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Root Out Bias in Decision‐making 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstand Why It Occurs 50\u003c\/p\u003e \u003cp\u003eTake Control on a Personal Level 50\u003c\/p\u003e \u003cp\u003eSolid Scientific Footings 51\u003c\/p\u003e \u003cp\u003e\u003ci\u003eProblem 1 \u003c\/i\u003e52\u003c\/p\u003e \u003cp\u003e\u003ci\u003eProblem 2 \u003c\/i\u003e52\u003c\/p\u003e \u003cp\u003eImplications 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Teach, Teach, Teach 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Rope Exercise 55\u003c\/p\u003e \u003cp\u003eThe “Roll Your Own” Exercise 56\u003c\/p\u003e \u003cp\u003eThe Starter Kit of Questions to Ask Data Scientists 59\u003c\/p\u003e \u003cp\u003eImplications 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Evaluating Data Science Outputs More Formally 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAssessing Information Quality 63\u003c\/p\u003e \u003cp\u003eA Hands‐On Information Quality Workshop 64\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePhase I: Individual Work \u003c\/i\u003e64\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePhase II: Teamwork \u003c\/i\u003e65\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePhase III: Group Presentation \u003c\/i\u003e66\u003c\/p\u003e \u003cp\u003eImplications 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Educating Senior Leaders 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCovering the Waterfront 68\u003c\/p\u003e \u003cp\u003eCompanies Need a Data and Data Science Strategy 70\u003c\/p\u003e \u003cp\u003eOrganizations Are “Unfit for Data” 71\u003c\/p\u003e \u003cp\u003eGet Started with Data Quality 71\u003c\/p\u003e \u003cp\u003eImplications 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Putting Data Science, and Data Scientists, in the Right Spots 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Need for Senior Leadership 73\u003c\/p\u003e \u003cp\u003eBuilding a Network of Data Scientists 74\u003c\/p\u003e \u003cp\u003eImplications 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Moving Up the Analytics Maturity Ladder 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eImplications 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 The Industrial Revolutions and Data Science 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe First Industrial Revolution: From Craft to Repetitive Activity 84\u003c\/p\u003e \u003cp\u003eThe Second Industrial Revolution: The Advent of the Factory 84\u003c\/p\u003e \u003cp\u003eThe Third Industrial Revolution: Enter the Computer 84\u003c\/p\u003e \u003cp\u003eThe Fourth Industrial Revolution: The Industry 4.0 Transformation 85\u003c\/p\u003e \u003cp\u003eImplications 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Epilogue 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStrong Foundations 87\u003c\/p\u003e \u003cp\u003eA Bridge to the Future 88\u003c\/p\u003e \u003cp\u003eAppendix A: Skills of a Data Scientist 91\u003c\/p\u003e \u003cp\u003eAppendix B: Data Defined 93\u003c\/p\u003e \u003cp\u003eAppendix C: Questions to Help Evaluate the Outputs of Data Science 95\u003c\/p\u003e \u003cp\u003eAppendix D: Ethical Considerations and Today’s Data Scientist 97\u003c\/p\u003e \u003cp\u003eAppendix E: Recent Technical Advances in Data Science 99\u003c\/p\u003e \u003cp\u003eReferences 101\u003c\/p\u003e \u003cp\u003eA List of Useful Links 107\u003c\/p\u003e \u003cp\u003eIndex 111\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eRON S. KENETT\u003c\/b\u003e is Chairman of the KPA Group, Israel, Senior Research Fellow at the Samuel Neaman Institute, Technion, Haifa and, previously, Professor of Operations Management, State University of New York, Binghamton, New York and President of the European Network for Business and Industrial Statistics. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eTHOMAS C. REDMAN,\u003c\/b\u003e \"the Data Doc,\" is the President of Data Quality Solutions. He helps leaders and companies understand their most important issues and opportunities in the data, chart a course, and build the organizational capabilities they need to execute.    \u003c\/p\u003e\u003cp\u003eThe essential guide for data scientists and for leaders who must get more from their data science teams \u003c\/p\u003e\u003cp\u003e\u003ci\u003eThe Economist\u003c\/i\u003e 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. \u003ci\u003eThe Real Work of Data Science\u003c\/i\u003e explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that 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.\" \u003c\/p\u003e\u003cp\u003e\"These two authors are world-class experts on analytics, data management, and data quality; they've forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it.\" \u003cb\u003e—Thomas H. Davenport,\u003c\/b\u003e Distinguished Professor, Babson College and Fellow,   MIT Initiative on the Digital Economy \u003c\/p\u003e\u003cp\u003e\"I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today's issues, such as computational Big Data.\" \u003cb\u003e —Sir David Cox,\u003c\/b\u003e Warden of Nuffield College and Professor of Statistics, Oxford University \u003c\/p\u003e\u003cp\u003e\"Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers.\" \u003cb\u003e —A. Blanton Godfrey,\u003c\/b\u003e Joseph D. Moore Distinguished University Professor,  Wilson College of Textiles, North Carolina State University\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990327148773,"sku":"NP9781119570707","price":38.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119570707.jpg?v=1761787371","url":"https:\/\/k12savings.com\/es\/products\/the-real-work-of-data-science-isbn-9781119570707","provider":"K12savings","version":"1.0","type":"link"}