Data Science Thinking

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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.

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What Is Data Science?

Chapter © 2019

Data Science

Chapter © 2019

What Data Scientists Can Learn from History

Chapter © 2015

Keywords

Table of contents (12 chapters)

Front Matter

Concepts and Thinking

Front Matter

The Data Science Era

What Is Data Science

Pages 29-58

Data Science Thinking

Pages 59-90

Challenges and Foundations

Front Matter

Pages 91-91

Data Science Challenges

Pages 93-128

Data Science Discipline

Pages 129-160

Data Science Foundations

Pages 161-202

Data Science Techniques

Pages 203-234

Industrialization and Opportunities

Front Matter

Pages 235-235

Data Economy and Industrialization

Pages 237-262

Data Science Applications

Pages 263-292

Data Profession

Pages 293-327

Data Science Education

Pages 329-348

Prospects and Opportunities in Data Science

Pages 349-362

Back Matter

Pages 363-390

Authors and Affiliations

Advanced Analytics Institute, University of Technology Sydney, Sydney, Australia

About the author

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.

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Price includes VAT (France)

Softcover Book EUR 79.11

Price includes VAT (France)

Hardcover Book EUR 79.11

Price includes VAT (France)

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