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Network Science Models for Data Analytics Automation - Xin W. Chen - Bog

Bag om Network Science Models for Data Analytics Automation

This book explains network science and its applications in data analytics for critical infrastructures, engineered systems, and knowledge acquisition. Each chapter describes step-by-step processes of how network science enables and automates data analytics through examples. The book not only dissects modeling techniques and analytical results but also explores the intrinsic development of these models and analyses. This unique approach bridges the gap between theory and practice and channels¿ managerial and problem-solving skills. Engineers, researchers, and managers would benefit from the extensive theoretical background and practical examples discussed in this book. Advanced undergraduate students and graduate students in mathematics, statistics, engineering, business, public health, and social science may use this book as a one-semester textbook or a reference book. Readers who are more interested in applications may skip Chapter 1 and peruse through the rest of the book with ease.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783030964696
  • Indbinding:
  • Hardback
  • Sideantal:
  • 122
  • Udgivet:
  • 22. Februar 2022
  • Udgave:
  • 12022
  • Størrelse:
  • 155x235x0 mm.
  • Vægt:
  • 366 g.
  • 8-11 hverdage.
  • 20. Juli 2024

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Medlemspris

Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Network Science Models for Data Analytics Automation

This book explains network science and its applications in data analytics for critical infrastructures, engineered systems, and knowledge acquisition. Each chapter describes step-by-step processes of how network science enables and automates data analytics through examples. The book not only dissects modeling techniques and analytical results but also explores the intrinsic development of these models and analyses. This unique approach bridges the gap between theory and practice and channels¿ managerial and problem-solving skills.

Engineers, researchers, and managers would benefit from the extensive theoretical background and practical examples discussed in this book. Advanced undergraduate students and graduate students in mathematics, statistics, engineering, business, public health, and social science may use this book as a one-semester textbook or a reference book. Readers who are more interested in applications may skip Chapter 1 and peruse through the rest of the book with ease.

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