Cao, Longbing.

Data Science Thinking : The Next Scientific, Technological and Economic Revolution. - 1 online resource (404 pages) - eBooks on Demand Data Analytics Ser. . - Data Analytics Ser. .

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 Initiatives -- 1.7.2 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 Statistics -- 2.5.2 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 Data -- 2.7.3 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 Thinking -- 3.2.2.4 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 -- 3.5.3 Data-Driven Scientific Discovery -- 3.5.3.1 What Is Data-Driven Discovery -- 3.5.3.2 Data-Driven Discovery vs. Model-Based Design -- 3.5.4 Data-Driven and Other Paradigms -- 3.5.4.1 Various Hybrid Paradigms -- 3.5.4.2 Domain + Data-Driven Discovery -- 3.5.5 Essential Questions to Ask in Data Science -- 3.6 Summary -- Part II Challenges and Foundations -- 4 Data Science Challenges -- 4.1 Introduction -- 4.2 X-Complexities in Data Science -- 4.2.1 Data Complexity -- 4.2.2 Behavior Complexity -- 4.2.3 Domain Complexity -- 4.2.4 Social Complexity -- 4.2.5 Environment Complexity -- 4.2.6 Human-Machine-Cooperation Complexity -- 4.2.7 Learning Complexity. 4.2.8 Deliverable Complexity -- 4.3 X-Intelligence in Data Science -- 4.3.1 Data Intelligence -- 4.3.2 Behavior Intelligence -- 4.3.3 Domain Intelligence -- 4.3.4 Human Intelligence -- 4.3.5 Network Intelligence -- 4.3.6 Organization Intelligence -- 4.3.7 Social Intelligence -- 4.3.8 Environment Intelligence -- 4.4 Known-to-Unknown Data-Capability-Knowledge Cognitive Path -- 4.4.1 The Data Science Cognitive Path -- 4.4.2 Four Knowledge Spaces in Data Science -- 4.4.3 Data Science Known-to-Unknown Evolution -- 4.4.4 Opportunities for Significant Original Invention -- 4.5 Non-IIDness in Data Science Problems -- 4.5.1 IIDness vs. Non-IIDness -- 4.5.2 Non-IID Challenges -- 4.6 Human-Like Machine Intelligence Revolution -- 4.6.1 Next-Generation Artificial Intelligence: Human-Like Machine Intelligence -- 4.6.2 Data Science-Enabled Human-Like Machine Intelligence -- 4.7 Data Quality -- 4.7.1 Data Quality Issues -- 4.7.2 Data Quality Metrics -- 4.7.3 Data Quality Assurance and Control -- 4.7.4 Data Quality Analytics -- 4.7.5 Data Quality Checklist -- 4.8 Data Social and Ethical Issues -- 4.8.1 Data Social Issues -- 4.8.2 Data Science Ethics -- 4.8.3 Data Ethics Assurance -- 4.9 The Extreme Data Challenge -- 4.10 Summary -- 5 Data Science Discipline -- 5.1 Introduction -- 5.2 Data-Capability Disciplinary Gaps -- 5.3 Methodologies for Complex Data Science Problems -- 5.3.1 From Reductionism and Holism to Systematism -- 5.3.1.1 Bottom-Up Reductionism -- 5.3.1.2 Top-Down Holism -- 5.3.1.3 Integrative Systematism -- 5.3.1.4 Appropriate Methodological Adoption -- 5.3.2 Synthesizing X-Intelligence -- 5.3.3 Qualitative-to-Quantitative Metasynthesis -- 5.4 Data Science Disciplinary Framework -- 5.4.1 Interdisciplinary Fusion for Data Science -- 5.4.2 Data Science Research Map -- 5.4.3 Systematic Research Approaches -- 5.4.4 Data A-Z for Data Science. 5.5 Some Essential Data Science Research Areas -- 5.5.1 Developing Data Science Thinking -- 5.5.1.1 Aspects of Data Science Thinking -- 5.5.1.2 Tasks in Developing Data Science Thinking -- 5.5.2 Understanding Data Characteristics and Complexities -- 5.5.3 Discovering Deep Behavior Insight -- 5.5.3.1 Physical World-Data World-Behavior World -- 5.5.3.2 Behavior Informatics for Discovering Behavior Insights -- 5.5.4 Fusing Data Science with Social and Management Science -- 5.5.4.1 Complementarity of Data Science and Social and Management Science -- 5.5.4.2 Interdisciplinary Areas and Capability Set -- 5.5.4.3 Creating New Interdisciplinary Areas and Professionals -- 5.5.5 Developing Analytics Repositories and Autonomous Data Systems -- 5.5.5.1 Benchmarks of Existing Analytics Systems -- 5.5.5.2 Towards Autonomous Analytics and Learning Systems -- 5.6 Summary -- 6 Data Science Foundations -- 6.1 Introduction -- 6.2 Cognitive Science and Brain Science for Data Science -- 6.3 Statistics and Data Science -- 6.3.1 Statistics for Data Science -- 6.3.2 Data Science for Statistics -- 6.4 Information Science Meets Data Science -- 6.4.1 Analysis and Processing -- 6.4.2 Informatics for Data Science -- 6.4.3 General Information Technologies -- 6.5 Intelligence Science and Data Science -- 6.5.1 Pattern Recognition, Mining, Analytics and Learning -- 6.5.2 Nature-Inspired Computational Intelligence -- 6.5.3 Data Science: Beyond Information and Intelligence Science -- 6.6 Computing Meets Data Science -- 6.6.1 Computing for Data Science -- 6.6.2 Data Science for Computing -- 6.7 Social Science Meets Data Science -- 6.7.1 Social Science for Data Science -- 6.7.1.1 Involving Social Thinking and Methods -- 6.7.1.2 Inventing Social Data Science for Social Problem-Solving -- 6.7.1.3 Social Features in Data Science and Products -- 6.7.2 Data Science for Social Science. 6.7.2.1 Promoting Social Science Paradigm Shift and Cultural Change -- 6.7.2.2 Creating New Social Science Theories and Methods -- 6.7.2.3 Addressing Emerging Social, Legal and Global Issues with Data Science -- 6.7.3 Social Data Science -- 6.8 Management Meets Data Science -- 6.8.1 Management for Data Science -- 6.8.1.1 Addressing Management Issues in Data Science -- 6.8.1.2 Building Management Components in Data Science -- 6.8.2 Data Science for Management -- 6.8.3 Management Analytics and Data Science -- 6.9 Communication Studies Meets Data Science -- 6.10 Other Fundamentals and Electives -- 6.10.1 Broad Business, Management and Social Areas -- 6.10.2 Domain and Expert Knowledge -- 6.10.3 Invention, Innovation and Practice -- 6.11 Summary -- 7 Data Science Techniques -- 7.1 Introduction -- 7.2 The Problem of Analytics and Learning -- 7.3 The Conceptual Map of Data Science Techniques -- 7.3.1 Foundations of Data Science -- 7.3.2 Classic Analytics and Learning Techniques -- 7.3.3 Advanced Analytics and Learning Techniques -- 7.3.4 Assisting Techniques -- 7.3.4.1 Artificial Intelligence and Intelligent Systems -- 7.3.4.2 Smart Manufacturing -- 7.3.4.3 Big Data and Cloud Computing Techniques -- 7.3.4.4 Data Engineering Techniques -- 7.3.4.5 Internet of Things -- 7.3.4.6 Security and Privacy Protection Techniques -- 7.3.4.7 Other Assisting Techniques -- 7.4 Data-to-Insight-to-Decision Analytics and Learning -- 7.4.1 Past Data Analytics and Learning -- 7.4.2 Present Data Analytics and Learning -- 7.4.3 Future Data Analytics and Learning -- 7.4.4 Actionable Decision Discovery and Delivery -- 7.5 Descriptive-to-Predictive-to-Prescriptive Analytics -- 7.5.1 Stage 1: Descriptive Analytics and Business Reporting -- 7.5.2 Stage 2: Predictive Analytics/Learning and Business Analytics -- 7.5.3 Stage 3: Prescriptive Analytics and Decision Making. 7.5.4 Focus Shifting Between Analytics/Learning Stages.

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