Advertisement

Data Science Thinking

The Next Scientific, Technological and Economic Revolution

  • Longbing Cao

Part of the Data Analytics book series (DAANA)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Concepts and Thinking

    1. Front Matter
      Pages 1-1
    2. Longbing Cao
      Pages 3-28
    3. Longbing Cao
      Pages 29-58
    4. Longbing Cao
      Pages 59-90
  3. Challenges and Foundations

    1. Front Matter
      Pages 91-91
    2. Longbing Cao
      Pages 93-128
    3. Longbing Cao
      Pages 129-160
    4. Longbing Cao
      Pages 161-202
    5. Longbing Cao
      Pages 203-234
  4. Industrialization and Opportunities

    1. Front Matter
      Pages 235-235
    2. Longbing Cao
      Pages 237-262
    3. Longbing Cao
      Pages 263-292
    4. Longbing Cao
      Pages 293-327
    5. Longbing Cao
      Pages 329-348
    6. Longbing Cao
      Pages 349-362
  5. Back Matter
    Pages 363-390

About this book

Introduction

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.

Keywords

Data science Data economy Data education Data profession Big data analytics Advanced analytics Data analytics Data quality Statistics

Authors and affiliations

  1. 1.Advanced Analytics InstituteUniversity of Technology SydneySydneyAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-95092-1
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-95091-4
  • Online ISBN 978-3-319-95092-1
  • Series Print ISSN 2520-1859
  • Series Online ISSN 2520-1867
  • Buy this book on publisher's site