Data science thinking : the next scientific, technological and economic revolution / Longbing Cao.

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

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

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

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

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