Overview
- Offers a unique breadth of coverage, providing a comprehensive study of data science, technology and economy perspectives.
- Presents rich and deep thinking and insights of data-driven research, innovation, industrialization, and opportunities
- Addresses the needs of decisions-makers who are responsible for managing the new realm of data science.
- Investigates the mindset and skillset of data scientists to help define the projected course of the data revolution
Part of the book series: Data Analytics (DAANA)
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Table of contents (12 chapters)
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Concepts and Thinking
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Challenges and Foundations
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Industrialization and Opportunities
Keywords
About this book
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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Data Science Thinking
Book Subtitle: The Next Scientific, Technological and Economic Revolution
Authors: Longbing Cao
Series Title: Data Analytics
DOI: https://doi.org/10.1007/978-3-319-95092-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-95091-4Published: 07 September 2018
Softcover ISBN: 978-3-030-06975-9Published: 31 January 2019
eBook ISBN: 978-3-319-95092-1Published: 17 August 2018
Series ISSN: 2520-1859
Series E-ISSN: 2520-1867
Edition Number: 1
Number of Pages: XX, 390
Number of Illustrations: 1 b/w illustrations, 61 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Big Data/Analytics, Artificial Intelligence