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Real Work of Data Science: Turning data into information, better decisions, and stronger organizations [Pehme köide]

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, (KPA Ltd., Israel)
  • Formaat: Paperback / softback, 144 pages, kõrgus x laius x paksus: 239x168x13 mm, kaal: 249 g
  • Ilmumisaeg: 12-Apr-2019
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119570700
  • ISBN-13: 9781119570707
Teised raamatud teemal:
Real Work of Data Science: Turning data into information, better decisions, and stronger organizationsSuurem pilt
  • Formaat: Paperback / softback, 144 pages, kõrgus x laius x paksus: 239x168x13 mm, kaal: 249 g
  • Ilmumisaeg: 12-Apr-2019
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119570700
  • ISBN-13: 9781119570707
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119570707.html
Märksõnad:

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. The Real Work of Data Science 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."

"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."
—Thomas H. Davenport, Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy

"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."
—Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University

"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." 
—A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University

About the Authors xv
Preface xvii
About the Companion Website xxi
1 A Higher Calling
1(8)
The Life-Cycle View
2(4)
Problem Elicitation: Understand the Problem
3(1)
Goal Formulation: Clarify the Short-term and Long-term Goals
3(1)
Data Collection: Identify Relevant Data Sources and Collect the Data
3(1)
Data Analysis: Use Descriptive, Explanatory, and Predictive Methods
3(1)
Formulation of Findings: State Results and Recommendations
4(1)
Operationalization of Findings: Suggest Who, What, When, and How
5(1)
Communication of Findings: Communicate Findings, Decisions, and Their Implications to Stakeholders
5(1)
Impact Assessment: Plan and Deploy an Assessment Strategy
5(1)
The Organizational Ecosystem
6(1)
Organizational Structure
6(1)
Organizational Maturity
6(1)
Once Again, Our Goal
6(3)
2 The Difference Between a Good Data Scientist and a Great One
9(4)
Implications
11(2)
3 Learn the Business
13(4)
The Annual Report
13(1)
SWOTs and Strategic Analysis
13(1)
The Balanced Scorecard and Key Performance Indicators
14(1)
The Data Lens
15(1)
Build Your Network
16(1)
Implications
16(1)
4 Understand the Real Problem
17(4)
A Telling Example
17(1)
Understanding the Real Problem
18(1)
Implications
19(2)
5 Get Out There
21(4)
Understand Context and Soft Data
21(1)
Identify Sources of Variability
22(1)
Selective Attention
23(1)
Memory Bias
23(1)
Implications
23(2)
6 Sorry, but You Can't Trust the Data
25(6)
Most Data Is Untrustworthy
25(2)
Dealing with Immediate Issues
27(2)
Getting in Front of Tomorrow's Data Quality Issues
29(1)
Implications
30(1)
7 Make It Easy for People to Understand Your Insights
31(4)
First, Get the Basics Right
31(2)
Presentations Get Passed Around
33(1)
The Best of the Best
34(1)
Implications
34(1)
8 When the Data Leaves Off and Your Intuition Takes Over
35(4)
Modes of Generalization
36(2)
Implications
38(1)
9 Take Accountability for Results
39(4)
Practical Statistical Efficiency
39(2)
Using Data Science to Perform Impact Analysis
41(1)
Implications
42(1)
10 What It Means to Be "Data-driven"
43(6)
Data-driven Companies and People
43(1)
Traits of the Data-driven
44(2)
Traits of the Amis
46(1)
Implications
46(3)
11 Root Out Bias in Decision-making
49(6)
Understand Why It Occurs
50(1)
Take Control on a Personal Level
50(1)
Solid Scientific Footings
51(2)
Problem 1
52(1)
Problem 2
52(1)
Implications
53(2)
12 Teach, Teach, Teach
55(8)
The Rope Exercise
55(1)
The "Roll Your Own" Exercise
56(3)
The Starter Kit of Questions to Ask Data Scientists
59(1)
Implications
60(3)
13 Evaluating Data Science Outputs More Formally
63(4)
Assessing Information Quality
63(1)
A Hands-On Information Quality Workshop
64(2)
Phase I Individual Work
64(1)
Phase II Teamwork
65(1)
Phase III Group Presentation
66(1)
Implications
66(1)
14 Educating Senior Leaders
67(6)
Covering the Waterfront
68(2)
Companies Need a Data and Data Science Strategy
70(1)
Organizations Are "Unfit for Data"
71(1)
Get Started with Data Quality
71(1)
Implications
71(2)
15 Putting Data Science, and Data Scientists, in the Right Spots
73(4)
The Need for Senior Leadership
73(1)
Building a Network of Data Scientists
74(2)
Implications
76(1)
16 Moving Up the Analytics Maturity Ladder
77(6)
Implications
81(2)
17 The Industrial Revolutions and Data Science
83(4)
The First Industrial Revolution: From Craft to Repetitive Activity
84(1)
The Second Industrial Revolution: The Advent of the Factory
84(1)
The Third Industrial Revolution: Enter the Computer
84(1)
The Fourth Industrial Revolution: The Industry 4.0 Transformation
85(1)
Implications
85(2)
18 Epilogue
87(4)
Strong Foundations
87(1)
A Bridge to the Future
88(3)
Appendix A Skills of a Data Scientist 91(2)
Appendix B Data Defined 93(2)
Appendix C Questions to Help Evaluate the Outputs of Data Science 95(2)
Appendix D Ethical Considerations and Today's Data Scientist 97(2)
Appendix E Recent Technical Advances in Data Science 99(2)
References 101(6)
A List of Useful Links 107(4)
Index 111
RON S. KENETT 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.

THOMAS C. REDMAN, "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.