Cao Longbing. Data Science Thinking

Springer, 2018. — 404 p. — ISBN 978-3-319-95092-1.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.Concepts and Thinking
The Data Science EraFeatures of the Data Era
The Data Science Journey
Data-Empowered Landscape
New X-Generations
The Interest Trends
Major Data Strategies by Governments
The Scientific Agenda for Data ScienceWhat Is Data ScienceDatafication and Data Quantification
Data, Information, Knowledge, Intelligence and Wisdom
DataDNA
Data Science Views
Definitions of Data Science
Open Model, Open Data and Open Science
Data Products
Myths and Misconceptions
Science ThinkingThinking in Science
Dala Science Structure
Data Science as a Complex System
Critical Thinking in Data ScienceChallenges and Foundations
Data Science ChallengesX-Complexities in Data Science
X-Inlelligence in Data Science
Known-to-Unknown Data-Capability-Knowledge Cognitive Path
Non-IIDness in Data Science Problems
Human-Like Machine Intelligence Revolution
Data Quality
Data Social and Ethical Issues
The Extreme Data ChallengeData Science DisciplineData-Capability Disciplinary Gaps
Methodologies for Complex Data Science Problems
Data Science Disciplinary Framework
Some Essential Data Science Research AreasData Science FoundationsCognitive Science and Brain Science for Data Science
Statistics and Data Science
Information Science Meets Data Science
Intelligence Science and Data Science
Computing Meets Data Science
Social Science Meets Data Science
Management Meets Data Science
Communication Studies Meets Data Science
Other Fundamentals and ElectivesData Science TechniquesThe Problem of Analytics and Learning
The Conceptual Map of Data Science Techniques
Data-to-Insight-to-Decision Analytics and Learning
Descriptive-to-Predictive-to-Prescriptive Analytics
X-AnalyticsIndustrialization and Opportunities
Data Economy and IndustrializationData Economy
Data Industry
Data ServicesData Science ApplicationsSome General Application Guidance
Advertising
Aerospace and Astronomy
Arts, Creative Design and Humanities
Bioinformatics
Consulting Services
Ecology and Environment
E-Commerce and Retail
Education
Engineering
Finance and Economy
Gaming Industry
Government
Healthcare and Clinics
Living, Sports, Entertainment, and Relevant Services
Management, Operations and Planning
Manufacturing
Marketing and Sales
Medicine
Physical and Virtual Societm, Community, Networks, Markets and Crowds
Publishing and Media
Recommendation Services
Science
Security and Safety
Social Sciences and Social Problems
Sustainability
Telecommunications and Mobile Services
Tourism and Travel
TransportationData ProfessionData Profession Formation
Data Science Roles
Core Data Science Knowledge and Skills
Data Science Maturity
Data Scientists
Data Engineers
Tools for Data ProfessionalsScience EducationData Science Course Review
Data Science Education FrameworkProspects and Opportunities in Data ScienceThe Fourth Revolution: Data+lntelligence Science, Technology and Economy
Data Science of Sciences
Data Brain
Machine intelligence and Thinking
Advancing Data Science and Technology and Economy
Advancing Data Education and Profession