| Description |
1 online resource (xx, 390 pages) |
| Series |
Data analytics |
|
Data analytics.
|
| Contents |
Intro; Preface; Acknowledgments; Contents; Part I Concepts and Thinking; 1 The Data Science Era; 1.1 Introduction; 1.2 Features of the Data Era; 1.2.1 Some Key Terms in Data Science; 1.2.2 Observations of the Data Era Debate; 1.2.3 Iconic Features and Trends of the Data Era; 1.3 The Data Science Journey; 1.3.1 New-Generation Data Products and Economy; 1.4 Data-Empowered Landscape; 1.4.1 Data Power; 1.4.2 Data-Oriented Forces; 1.5 New X-Generations; 1.5.1 X-Complexities; 1.5.2 X-Intelligence; 1.5.3 X-Opportunities; 1.6 The Interest Trends; 1.7 Major Data Strategies by Governments |
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1.7.1 Governmental Data Initiatives1.7.2 Australian Initiatives; 1.7.3 Chinese Initiatives; 1.7.4 European Initiatives; 1.7.5 United States' Initiatives; 1.7.6 Other Governmental Initiatives; 1.8 The Scientific Agenda for Data Science; 1.8.1 The Scientific Agenda by Governments; 1.8.2 Data Science Research Initiatives; 1.9 Summary; 2 What Is Data Science; 2.1 Introduction; 2.2 Datafication and Data Quantification; 2.3 Data, Information, Knowledge, Intelligence and Wisdom; 2.4 Data DNA; 2.4.1 What Is Data DNA; 2.4.2 Data DNA Functionalities; 2.5 Data Science Views |
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2.5.1 The Data Science View in Statistics2.5.2 A Multidisciplinary Data Science View; 2.5.3 The Data-Centric View; 2.6 Definitions of Data Science; 2.6.1 High-Level Data Science Definition; 2.6.2 Trans-Disciplinary Data Science Definition; 2.6.3 Process-Based Data Science Definition; 2.6.3.1 Thinking with Wisdom; 2.6.3.2 Understanding the Domain; 2.6.3.3 Managing Data; 2.6.3.4 Computing with Data; 2.6.3.5 Discovering Knowledge; 2.6.3.6 Communicating with Stakeholders; 2.6.3.7 Delivering Data Products; 2.6.3.8 Acting on Insights; 2.7 Open Model, Open Data and Open Science; 2.7.1 Open Model |
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2.7.2 Open Data2.7.3 Open Science; 2.8 Data Products; 2.9 Myths and Misconceptions; 2.9.1 Possible Negative Effects in Conducting Data Science; 2.9.2 Conceptual Misconceptions; 2.9.3 Data Volume Misconceptions; 2.9.4 Data Infrastructure Misconceptions; 2.9.5 Analytics Misconceptions; 2.9.6 Misconceptions About Capabilities and Roles; 2.9.7 Other Matters; 2.10 Summary; 3 Data Science Thinking; 3.1 Introduction; 3.2 Thinking in Science; 3.2.1 Scientific vs. Unscientific Thinking; 3.2.2 Creative Thinking vs. Logical Thinking; 3.2.2.1 Logical Thinking; 3.2.2.2 Creative Thinking |
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3.2.2.3 Critical Thinking3.2.2.4 Lateral Thinking; 3.3 Data Science Structure; 3.4 Data Science as a Complex System; 3.4.1 A Systematic View of Data Science Problems; 3.4.2 Complexities in Data Science Systems; 3.4.3 The Framework for Data Science Thinking; 3.4.4 Data Science Thought; 3.4.5 Data Science Custody; 3.4.6 Data Science Feed; 3.4.7 Mechanism Design for Data Science; 3.4.8 Data Science Deliverables; 3.4.9 Data Science Assurance; 3.5 Critical Thinking in Data Science; 3.5.1 Critical Thinking Perspectives; 3.5.2 We Do Not Know What We Do Not Know |
| Summary |
This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. The topics cover an extremely wide spectrum of essential and relevant aspects of data science, spanning its evolution, concepts, thinking, challenges, discipline, and foundation, all the way to industrialization, profession, education, and the vast array of opportunities that data science offers. The book's three parts each detail layers of these different aspects. The book is intended for decision-makers, data managers (e.g., analytics portfolio managers, business analytics managers, chief data analytics officers, chief data scientists, and chief data officers), policy makers, management and decision strategists, research leaders, and educators who are responsible for pursuing new scientific, innovation, and industrial transformation agendas, enterprise strategic planning, a next-generation profession-oriented course development, as well as those who are involved in data science, technology, and economy from an advanced perspective. Research students in data science-related courses and disciplines will find the book useful for positing their innovative scientific journey, planning their unique and promising career, and competing within and being ready for the next generation of science, technology, and economy |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from digital title page (viewed on September 12, 2018) |
| Subject |
Big data.
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Data mining.
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Information science.
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Data structures (Computer science)
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Database management.
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Cyberinfrastructure.
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Data Mining
|
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information science.
|
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Business mathematics & systems.
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Artificial intelligence.
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Data mining.
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COMPUTERS -- Databases -- General.
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Big data
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Cyberinfrastructure
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Data mining
|
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Data structures (Computer science)
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Database management
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Information science
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| Form |
Electronic book
|
| ISBN |
9783319950921 |
|
3319950924 |
|
9783319950938 |
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3319950932 |
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