Description |
1 online resource (231 p.) |
Series |
Chapman and Hall/CRC Data Science Ser |
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Chapman and Hall/CRC Data Science Ser
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Contents |
Cover -- Endorsement -- Half Title -- Series Information -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Preface -- Acknowledgements -- Contributors -- 1 Introduction -- Data Science as a Sociotechnical Capability -- The Fallacy of Strategic Alignment -- A Tale of Two Databases -- The Wickedness of Building Data Capabilities -- The Notion of Emergent Design -- What to Expect From This Book -- The Structure of the Book -- Notes -- References -- 2 What Is Data Science? -- The Data Analytics Stack -- Data Ingestion -- Storage -- Access |
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BI vs Analytics vs Data Science -- Are You Ready for Data Science? -- The Data Science Process -- Doing the Thing -- Machine Learning Problem Types -- Test Your Knowledge -- What About AI? -- Great Power, Narrow Focus -- Doing the Thing Right -- Doing the Right Thing -- In Closing -- Notes -- References -- 3 The Principles of Emergent Design -- The Origins of Emergent Design -- Is There a Better Way? -- Emergent Design, Evolution, and Learning -- Uncertainty and Ambiguity -- Guidelines for Emergent Design -- Be a Midwife Rather Than an Expert -- Use Conversations to Gain Commitment |
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Understand and Address Concerns of Stakeholders Who Are Wary of the Change -- Frame the Current Situation as an Enabling Constraint -- Consider Long-Term and Hidden Consequences -- Create an Environment That Encourages Learning -- Beware of Platitudinous Goals -- Act So as to Increase Future Choices -- Putting Emergent Design to Work -- An Illustrative Case Study -- Background -- The Route to Emergent Design -- A Pivotal Conversation -- First Steps -- Integrating the New Capability -- The "Pilot" Project -- Scaling Up -- The Official OK -- Lessons Learnt -- Summarising -- Notes -- References |
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4 Charting a Course -- Introduction -- Tackling the Corporate Immune System -- Finding Problems -- Demonstrating Value -- Additional Benefits of "Problem Finding" -- Powerful Questions -- Designing for the Future -- Notes -- References -- 5 Capability and Culture -- Introduction -- Data Talent Archetypes -- Database Administrators, Data Engineers, and Data Warehouse Architects -- Key Skills -- What They Do -- What They Don't Do -- Business Intelligence (BI) Developers and Analysts -- Key Skills -- What They Do -- What They Don't Do -- From Analyst to Data Scientist -- Key Skills |
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What They Can Do for You -- What They Don't Do -- Newer Data Roles -- Other Roles -- The Right Timing -- Building Capability -- Identifying and Growing Talent -- Designing Training Programmes -- Immersion and Other Approaches -- Ongoing Development -- "Building a Culture" -- Communities of Practice -- Data Literacy -- Critical Thinking -- Problems, Hypotheses, and the Scientific Method -- Measuring Culture -- Some Principles for Developing a Data Culture -- Buying Talent -- Find the Right Mix of Skills -- Value Problem-Solving -- Assessing Broader Skills |
Summary |
This book describes how to establish data science and analytics capabilities in organisations using emergent design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment |
Notes |
Description based upon print version of record |
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Get the Candidate to Work On a Real Data Problem |
Form |
Electronic book
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Author |
Scriven, Alexander
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ISBN |
9781000859454 |
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1000859452 |
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