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Data Science Thinking: The Next Scientific, Technological and Economic Revolution 2018 ed. [Kõva köide]

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  • Formaat: Hardback, 390 pages, kõrgus x laius: 235x155 mm, kaal: 781 g, 61 Illustrations, color; 1 Illustrations, black and white; XX, 390 p. 62 illus., 61 illus. in color., 1 Hardback
  • Sari: Data Analytics
  • Ilmumisaeg: 07-Sep-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319950916
  • ISBN-13: 9783319950914
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Data Science Thinking: The Next Scientific, Technological and Economic Revolution 2018 ed.Suurem pilt
  • Formaat: Hardback, 390 pages, kõrgus x laius: 235x155 mm, kaal: 781 g, 61 Illustrations, color; 1 Illustrations, black and white; XX, 390 p. 62 illus., 61 illus. in color., 1 Hardback
  • Sari: Data Analytics
  • Ilmumisaeg: 07-Sep-2018
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319950916
  • ISBN-13: 9783319950914
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319950914.html

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.

Part I Concepts and Thinking
1 The Data Science Era
3(26)
1.1 Introduction
3(2)
1.2 Features of the Data Era
5(4)
1.2.1 Some Key Terms in Data Science
5(1)
1.2.2 Observations of the Data Era Debate
5(2)
1.2.3 Iconic Features and Trends of the Data Era
7(2)
1.3 The Data Science Journey
9(5)
1.3.1 New-Generation Data Products and Economy
13(1)
1.4 Data-Empowered Landscape
14(3)
1.4.1 Data Power
14(2)
1.4.2 Data-Oriented Forces
16(1)
1.5 New X-Generations
17(3)
1.5.1 X-Complexities
18(1)
1.5.2 X-Intelligence
18(1)
1.5.3 X-Opportunities
19(1)
1.6 The Interest Trends
20(1)
1.7 Major Data Strategies by Governments
21(5)
1.7.1 Governmental Data Initiatives
23(1)
1.7.2 Australian Initiatives
23(1)
1.7.3 Chinese Initiatives
24(1)
1.7.4 European Initiatives
25(1)
1.7.5 United States' Initiatives
25(1)
1.7.6 Other Governmental Initiatives
26(1)
1.8 The Scientific Agenda for Data Science
26(2)
1.8.1 The Scientific Agenda by Governments
26(1)
1.8.2 Data Science Research Initiatives
27(1)
1.9 Summary
28(1)
2 What Is Data Science
29(30)
2.1 Introduction
29(1)
2.2 Datafication and Data Quantification
29(1)
2.3 Data, Information, Knowledge, Intelligence and Wisdom
30(2)
2.4 Data DNA
32(2)
2.4.1 What Is Data DNA
32(1)
2.4.2 Data DNA Functionalities
33(1)
2.5 Data Science Views
34(2)
2.5.1 The Data Science View in Statistics
34(1)
2.5.2 A Multidisciplinary Data Science View
35(1)
2.5.3 The Data-Centric View
35(1)
2.6 Definitions of Data Science
36(7)
2.6.1 High-Level Data Science Definition
36(1)
2.6.2 Trans-Disciplinary Data Science Definition
37(1)
2.6.3 Process-Based Data Science Definition
38(5)
2.7 Open Model, Open Data and Open Science
43(5)
2.7.1 Open Model
44(1)
2.7.2 Open Data
45(1)
2.7.3 Open Science
46(2)
2.8 Data Products
48(1)
2.9 Myths and Misconceptions
48(10)
2.9.1 Possible Negative Effects in Conducting Data Science
49(1)
2.9.2 Conceptual Misconceptions
50(2)
2.9.3 Data Volume Misconceptions
52(1)
2.9.4 Data Infrastructure Misconceptions
53(1)
2.9.5 Analytics Misconceptions
53(2)
2.9.6 Misconceptions About Capabilities and Roles
55(1)
2.9.7 Other Matters
56(2)
2.10 Summary
58(1)
3 Data Science Thinking
59(34)
3.1 Introduction
59(1)
3.2 Thinking in Science
60(6)
3.2.1 Scientific vs. Unscientific Thinking
60(2)
3.2.2 Creative Thinking vs. Logical Thinking
62(4)
3.3 Data Science Structure
66(2)
3.4 Data Science as a Complex System
68(9)
3.4.1 A Systematic View of Data Science Problems
68(3)
3.4.2 Complexities in Data Science Systems
71(1)
3.4.3 The Framework for Data Science Thinking
72(1)
3.4.4 Data Science Thought
73(1)
3.4.5 Data Science Custody
74(1)
3.4.6 Data Science Feed
74(1)
3.4.7 Mechanism Design for Data Science
75(1)
3.4.8 Data Science Deliverables
76(1)
3.4.9 Data Science Assurance
76(1)
3.5 Critical Thinking in Data Science
77(12)
3.5.1 Critical Thinking Perspectives
77(1)
3.5.2 We Do Not Know What We Do Not Know
77(3)
3.5.3 Data-Driven Scientific Discovery
80(3)
3.5.4 Data-Driven and Other Paradigms
83(5)
3.5.5 Essential Questions to Ask in Data Science
88(1)
3.6 Summary
89(4)
Part II Challenges and Foundations
4 Data Science Challenges
93(36)
4.1 Introduction
93(1)
4.2 X-Complexities in Data Science
94(5)
4.2.1 Data Complexity
94(1)
4.2.2 Behavior Complexity
95(1)
4.2.3 Domain Complexity
95(1)
4.2.4 Social Complexity
96(1)
4.2.5 Environment Complexity
96(1)
4.2.6 Human-Machine-Cooperation Complexity
97(1)
4.2.7 Learning Complexity
97(1)
4.2.8 Deliverable Complexity
98(1)
4.3 X-Intelligence in Data Science
99(4)
4.3.1 Data Intelligence
99(1)
4.3.2 Behavior Intelligence
100(1)
4.3.3 Domain Intelligence
100(1)
4.3.4 Human Intelligence
100(1)
4.3.5 Network Intelligence
101(1)
4.3.6 Organization Intelligence
101(1)
4.3.7 Social Intelligence
102(1)
4.3.8 Environment Intelligence
103(1)
4.4 Known-to-Unknown Data-Capability-Knowledge Cognitive Path
103(3)
4.4.1 The Data Science Cognitive Path
103(1)
4.4.2 Four Knowledge Spaces in Data Science
104(1)
4.4.3 Data Science Known-to-Unknown Evolution
105(1)
4.4.4 Opportunities for Significant Original Invention
105(1)
4.5 Non-IIDness in Data Science Problems
106(3)
4.5.1 IIDness vs. Non-IIDness
106(2)
4.5.2 Non-IID Challenges
108(1)
4.6 Human-Like Machine Intelligence Revolution
109(4)
4.6.1 Next-Generation Artificial Intelligence: Human-Like Machine Intelligence
110(1)
4.6.2 Data Science-Enabled Human-Like Machine Intelligence
111(2)
4.7 Data Quality
113(8)
4.7.1 Data Quality Issues
113(2)
4.7.2 Data Quality Metrics
115(1)
4.7.3 Data Quality Assurance and Control
116(2)
4.7.4 Data Quality Analytics
118(1)
4.7.5 Data Quality Checklist
119(2)
4.8 Data Social and Ethical Issues
121(4)
4.8.1 Data Social Issues
121(2)
4.8.2 Data Science Ethics
123(1)
4.8.3 Data Ethics Assurance
124(1)
4.9 The Extreme Data Challenge
125(2)
4.10 Summary
127(2)
5 Data Science Discipline
129(32)
5.1 Introduction
129(1)
5.2 Data-Capability Disciplinary Gaps
129(2)
5.3 Methodologies for Complex Data Science Problems
131(7)
5.3.1 From Reductionism and Holism to Systematism
132(3)
5.3.2 Synthesizing X-Intelligence
135(1)
5.3.3 Qualitative-to-Quantitative Metasynthesis
136(2)
5.4 Data Science Disciplinary Framework
138(7)
5.4.1 Interdisciplinary Fusion for Data Science
138(2)
5.4.2 Data Science Research Map
140(3)
5.4.3 Systematic Research Approaches
143(1)
5.4.4 Data A-Z for Data Science
144(1)
5.5 Some Essential Data Science Research Areas
145(15)
5.5.1 Developing Data Science Thinking
146(2)
5.5.2 Understanding Data Characteristics and Complexities
148(2)
5.5.3 Discovering Deep Behavior Insight
150(3)
5.5.4 Fusing Data Science with Social and Management Science
153(3)
5.5.5 Developing Analytics Repositories and Autonomous Data Systems
156(4)
5.6 Summary
160(1)
6 Data Science Foundations
161(42)
6.1 Introduction
161(2)
6.2 Cognitive Science and Brain Science for Data Science
163(1)
6.3 Statistics and Data Science
164(3)
6.3.1 Statistics for Data Science
165(1)
6.3.2 Data Science for Statistics
166(1)
6.4 Information Science Meets Data Science
167(4)
6.4.1 Analysis and Processing
168(1)
6.4.2 Informatics for Data Science
169(1)
6.4.3 General Information Technologies
170(1)
6.5 Intelligence Science and Data Science
171(4)
6.5.1 Pattern Recognition, Mining, Analytics and Learning
172(1)
6.5.2 Nature-Inspired Computational Intelligence
173(1)
6.5.3 Data Science: Beyond Information and Intelligence Science
173(2)
6.6 Computing Meets Data Science
175(4)
6.6.1 Computing for Data Science
175(2)
6.6.2 Data Science for Computing
177(2)
6.7 Social Science Meets Data Science
179(11)
6.7.1 Social Science for Data Science
180(3)
6.7.2 Data Science for Social Science
183(5)
6.7.3 Social Data Science
188(2)
6.8 Management Meets Data Science
190(7)
6.8.1 Management for Data Science
191(3)
6.8.2 Data Science for Management
194(2)
6.8.3 Management Analytics and Data Science
196(1)
6.9 Communication Studies Meets Data Science
197(2)
6.10 Other Fundamentals and Electives
199(3)
6.10.1 Broad Business, Management and Social Areas
200(1)
6.10.2 Domain and Expert Knowledge
200(1)
6.10.3 Invention, Innovation and Practice
201(1)
6.11 Summary
202(1)
7 Data Science Techniques
203(34)
7.1 Introduction
203(1)
7.2 The Problem of Analytics and Learning
204(1)
7.3 The Conceptual Map of Data Science Techniques
204(15)
7.3.1 Foundations of Data Science
205(3)
7.3.2 Classic Analytics and Learning Techniques
208(2)
7.3.3 Advanced Analytics and Learning Techniques
210(4)
7.3.4 Assisting Techniques
214(5)
7.4 Data-to-Insight-to-Decision Analytics and Learning
219(3)
7.4.1 Past Data Analytics and Learning
220(1)
7.4.2 Present Data Analytics and Learning
220(1)
7.4.3 Future Data Analytics and Learning
221(1)
7.4.4 Actionable Decision Discovery and Delivery
221(1)
7.5 Descriptive-to-Predictive-to-Prescriptive Analytics
222(8)
7.5.1 Stage 1: Descriptive Analytics and Business Reporting
223(1)
7.5.2 Stage 2: Predictive Analytics/Learning and Business Analytics
224(1)
7.5.3 Stage 3: Prescriptive Analytics and Decision Making
225(1)
7.5.4 Focus Shifting Between Analytics/Learning Stages
226(2)
7.5.5 Synergizing Descriptive, Predictive and Prescriptive Analytics
228(2)
7.6 X-Analytics
230(2)
7.6.1 X-Analytics Spectrum
230(1)
7.6.2 X-Analytics Working Mechanism
231(1)
7.7 Summary
232(5)
Part III Industrialization and Opportunities
8 Data Economy and Industrialization
237(26)
8.1 Introduction
237(1)
8.2 Data Economy
237(14)
8.2.1 What Is Data Economy
238(3)
8.2.2 Data Economy Example: Smart Taxis and Shared e-Bikes
241(2)
8.2.3 New Data Economic Model
243(3)
8.2.4 Distinguishing Characteristics of Data Economy
246(1)
8.2.5 Intelligent Economy and Intelligent Datathings
247(2)
8.2.6 Translating Real Economy
249(2)
8.3 Data Industry
251(6)
8.3.1 Categories of Data Industries
251(1)
8.3.2 New Data Industries
252(2)
8.3.3 Transforming Traditional Industries
254(3)
8.4 Data Services
257(5)
8.4.1 Data Service Models
257(2)
8.4.2 Data Analytical Services
259(3)
8.5 Summary
262(1)
9 Data Science Applications
263(30)
9.1 Introduction
263(1)
9.2 Some General Application Guidance
264(5)
9.2.1 Data Science Application Scenarios
264(1)
9.2.2 General Data Science Processes
264(1)
9.2.3 General vs. Domain-Specific Algorithms and Vendor-Dependent vs. Independent Solutions
265(1)
9.2.4 The Waterfall Model vs. the Agile Model for Data Science Project Management
266(2)
9.2.5 Success Factors for Data Science Projects
268(1)
9.3 Advertising
269(1)
9.4 Aerospace and Astronomy
270(1)
9.5 Arts, Creative Design and Humanities
270(1)
9.6 Bioinformatics
271(1)
9.7 Consulting Services
271(1)
9.8 Ecology and Environment
272(1)
9.9 E-Commerce and Retail
273(1)
9.10 Education
274(1)
9.11 Engineering
274(1)
9.12 Finance and Economy
275(1)
9.13 Gaming Industry
276(1)
9.14 Government
277(1)
9.15 Healthcare and Clinics
277(1)
9.16 Living, Sports, Entertainment, and Relevant Services
278(1)
9.17 Management, Operations and Planning
279(1)
9.18 Manufacturing
279(1)
9.19 Marketing and Sales
280(1)
9.20 Medicine
281(1)
9.21 Physical and Virtual Society, Community, Networks Markets and Crowds
282(2)
9.22 Publishing and Media
284(1)
9.23 Recommendation Services
285(1)
9.24 Science
286(1)
9.25 Security and Safety
287(1)
9.26 Social Sciences and Social Problems
288(1)
9.27 Sustainability
288(1)
9.28 Telecommunications and Mobile Services
289(1)
9.29 Tourism and Travel
290(1)
9.30 Transportation
291(1)
9.31 Summary
292(1)
10 Data Profession
293(36)
10.1 Introduction
293(1)
10.2 Data Profession Formation
294(4)
10.2.1 Disciplinary Significance Indicator
294(1)
10.2.2 Significant Data Science Research
294(1)
10.2.3 Global Data Scientific Communities
295(2)
10.2.4 Significant Data Professional Development
297(1)
10.2.5 Significant Socio-Economic Development
298(1)
10.3 Data Science Roles
298(3)
10.3.1 Data Science Team
299(1)
10.3.2 Data Science Positions
300(1)
10.4 Core Data Science Knowledge and Skills
301(6)
10.4.1 Data Science Knowledge and Capability Set
301(3)
10.4.2 Data Science Communication Skills
304(3)
10.5 Data Science Maturity
307(6)
10.5.1 Data Science Maturity Model
308(1)
10.5.2 Data Maturity
309(2)
10.5.3 Capability Maturity
311(1)
10.5.4 Organizational Maturity
312(1)
10.6 Data Scientists
313(7)
10.6.1 Who Are Data Scientists
313(1)
10.6.2 Chief Data Scientists
314(1)
10.6.3 What Data Scientists Do
315(3)
10.6.4 Qualifications of Data Scientists
318(1)
10.6.5 Data Scientists vs. BI Professionals
319(1)
10.6.6 Data Scientist Job Survey
320(1)
10.7 Data Engineers
320(5)
10.7.1 Who Are Data Engineers
321(2)
10.7.2 What Data Engineers Do
323(2)
10.8 Tools for Data Professionals
325(1)
10.9 Summary
326(3)
11 Data Science Education
329(20)
11.1 Introduction
329(1)
11.2 Data Science Course Review
330(7)
11.2.1 Overview of Existing Courses
330(1)
11.2.2 Disciplines Offering Courses
331(1)
11.2.3 Course Body of Knowledge
332(1)
11.2.4 Course-Offering Organizations
332(1)
11.2.5 Course-Offering Channels
333(1)
11.2.6 Online Courses
333(1)
11.2.7 Gap Analysis of Existing Courses
334(3)
11.3 Data Science Education Framework
337(10)
11.3.1 Data Science Course Structure
337(2)
11.3.2 Bachelor in Data Science
339(4)
11.3.3 Master in Data Science
343(3)
11.3.4 PhD in Data Science
346(1)
11.4 Summary
347(2)
12 Prospects and Opportunities in Data Science
349(14)
12.1 Introduction
349(1)
12.2 The Fourth Revolution: Data+Intelligence Science Technology and Economy
350(5)
12.2.1 Data Science, Technology and Economy: An Emerging Area
350(2)
12.2.2 The Fourth Scientific, Technological and Economic Revolution
352(3)
12.3 Data Science of Sciences
355(1)
12.4 Data Brain
356(2)
12.5 Machine Intelligence and Thinking
358(1)
12.6 Advancing Data Science and Technology and Economy
359(2)
12.7 Advancing Data Education and Profession
361(1)
12.8 Summary
362(1)
References 363(18)
Index 381
Longbing Cao holds a PhD in Pattern Recognition and Intelligent Systems from the Chinese Academy of Sciences, China and another PhD in Computing Science at the University of Technology Sydney, Australia. He is a professor of data science at UTS. He has been working on data science and analytics research, education, development, and enterprise applications since he was a CTO and then joined academia. Motivated by real-world significant and common challenges, he has been leading the team to develop theories, tools and applications for new areas including non-IID learning, actionable knowledge discovery, behavior informatics, and complex intelligent systems, in addition to issues related to artificial intelligence, knowledge discovery, machine learning, and their enterprise applications. In data science and analytics, he initiated the Data Science and Knowledge Discovery lab at UTS in 2007, the Advanced Analytics Institute in 2011, the degrees Master of Analytics (Research) and PhD in Analytics in 2011 which are recognized as the world's first degrees in data science, the IEEE Task Force on Data Science and Advanced Analytics (DSAA) and IEEE Task Force on Behavior, Economic and Soci-cultural Computing in 2013, the IEEE Conference on Data Science and Advanced Analytics (DSAA), the ACM SIGKDD Australia and New Zealand Chapter in 2014, and the International Journal of Data Science and Analytics with Springer in 2015. He served as program and general chairs of conferences such as KDD2015. In enterprise data science innovation, his team has successfully delivered many large projects for government and business organizations in over 10 domains including finance/capital markets, banking, health and car insurance, health, telco, recommendation, online business, education, and the public sector including ATO, DFS, DHS, DIBP and IP Australia, resulting in billions of dollar savings and mentions in government, industry, media and OECD reports.