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Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things (Texts in Computer Science)

Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things (Texts in Computer Science)

Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing
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Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things (Texts in Computer Science) Paperback - 2022

by Hill, Richard/ Berry, Stuart

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Springer, 2022. Paperback. New. 300 pages. 9.25x6.10x0.71 inches.
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Details

  • Title Guide to Industrial Analytics: Solving Data Science Problems for Manufacturing and the Internet of Things (Texts in Computer Science)
  • Author Hill, Richard/ Berry, Stuart
  • Binding Paperback
  • Condition New
  • Pages 275
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2022
  • Illustrated Yes
  • Features Illustrated
  • Bookseller's Inventory # x-3030791068
  • ISBN 9783030791063 / 3030791068
  • Weight 0.94 lbs (0.43 kg)
  • Dimensions 9.21 x 6.14 x 0.63 in (23.39 x 15.60 x 1.60 cm)
  • Category Computers - Data Base Management
  • Quantity available 2

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From the publisher

This textbook describes the hands-on application of data science techniques to solve problems in manufacturing and the Industrial Internet of Things (IIoT). Monitoring and managing operational performance is a crucial activity for industrial and business organisations. The emergence of low-cost, accessible computing and storage, through Industrial Digital Technologies (IDT) and Industry 4.0, has generated considerable interest in innovative approaches to doing more with data.

Data science, predictive analytics, machine learning, artificial intelligence and general approaches to modelling, simulating and visualising industrial systems have often been considered topics only for research labs and academic departments.

This textbook debunks the mystique around applied data science and shows readers, using tutorial-style explanations and real-life case studies, how practitioners can develop their own understanding of performance to achieve tangible business improvements. All exercises can be completed with commonly available tools, many of which are free to install and use.

Readers will learn how to use tools to investigate, diagnose, propose and implement analytics solutions that will provide explainable results to deliver digital transformation.


From the rear cover

Monitoring and managing operational performance is a crucial activity for industrial and business organisations. The emergence of low cost, accessible computing and storage through the Industrial Internet of Things (IIoT) has generated considerable interest in innovative approaches to doing more with data.

Data Science, predictive analytics, machine learning, artificial intelligence and the more general approaches to modelling, simulating and visualizing industrial systems have often been considered topics only for research labs and academic departments. This book debunks the mystique around applied data science and shows readers, using tutorial-style explanations and real-life case studies, how practitioners can develop their own understanding of performance to achieve tangible business improvements.

Topics and features:

  • Describes hands-on application of data-science techniques to solve problems in manufacturing and the IIoT
  • Presents relevant case study examples that make use of commonly available (and often free) software to solve real-world problems
  • Enables readers to rapidly acquire a practical understanding of essential modelling and analytics skills for system-oriented problem solving
  • Includes a schedule to organize content for semester-based university delivery, and end-of-chapter exercises to reinforce learning

This unique textbook/guide outlines how to use tools to investigate, diagnose, propose and implement analytics solutions that will provide the evidence for business cases, or to deliver explainable results that demonstrate positive impact within an organisation. It will be invaluable to students, applications developers, researchers, technical consultants, and industrial managers and supervisors.

Dr. Richard Hill is a professor of Intelligent Systems, head of the Department of Computer Science, and director of the Centre for Industrial Analytics at the University of Huddersfield, UK. His other Springer titles include Guide to Vulnerability Analysis for Computer Networks and Systems and Big-Data Analytics and Cloud Computing. Dr. Stuart Berry is Emeritus Fellow in the Department of Computing and Mathematics at the University of Derby, UK. He is a co-editor of the Springer title, Guide to Computational Modelling for Decision Processes.

About the author

Dr. Richard Hill is Professor of Intelligent Systems, Head of the Department of Computer Science, and the Director of the Centre for Industrial Analytics at the University of Huddersfield, UK. His other publications include the Springer titles Guide to Vulnerability Analysis for Computer Networks and Systems, Guide to Security in SDN and NFV, Guide to Security Assurance for Cloud Computing, Big-Data Analytics and Cloud Computing, Guide to Cloud Computing, and Cloud Computing for Enterprise Architectures.

Dr. Stuart Berry is Emeritus Fellow in the Department of Computing and Mathematics at the University of Derby, UK. His other publications include the Springer title Guide to Computational Modelling for Decision Processes.


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