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Guide to Industrial Analytics
Solving Data Science Problems for Manufacturing and the Internet of Things
Buch von Stuart Berry (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor

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.

Zusammenfassung

Describes data science techniques for solving problems in manufacturing and the Industrial Internet of Things

Presents case study examples using commonly available software to solve real-world problems

Empowers a practical understanding of essential modeling and analytics skills for system-oriented problem solving

Inhaltsverzeichnis
1.Introduction to Industrial Analytics.- 2. Measuring Performance.- 3. Modelling and Simulating Systems.- 4. Optimising Systems.- 5. Production Control and Scheduling.- 6. Simulating Demand Forecasts.- 7. Investigating Time Series Data.- 8. Determining the Minimum Information for Effective Control.- 9. Constructing Machine Learning Models for Prediction.- 10. Exploring Model Accuracy.
Details
Medium: Buch
Reihe: Texts in Computer Science
Inhalt: xxv
275 S.
64 s/w Illustr.
108 farbige Illustr.
275 p. 172 illus.
108 illus. in color.
ISBN-13: 9783030791032
ISBN-10: 3030791033
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Berry, Stuart
Hill, Richard
Auflage: 1st ed. 2021
Hersteller: Springer International Publishing
Texts in Computer Science
Maße: 241 x 160 x 22 mm
Von/Mit: Stuart Berry (u. a.)
Erscheinungsdatum: 28.09.2021
Gewicht: 0,617 kg
Artikel-ID: 120095867
Über den Autor

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.

Zusammenfassung

Describes data science techniques for solving problems in manufacturing and the Industrial Internet of Things

Presents case study examples using commonly available software to solve real-world problems

Empowers a practical understanding of essential modeling and analytics skills for system-oriented problem solving

Inhaltsverzeichnis
1.Introduction to Industrial Analytics.- 2. Measuring Performance.- 3. Modelling and Simulating Systems.- 4. Optimising Systems.- 5. Production Control and Scheduling.- 6. Simulating Demand Forecasts.- 7. Investigating Time Series Data.- 8. Determining the Minimum Information for Effective Control.- 9. Constructing Machine Learning Models for Prediction.- 10. Exploring Model Accuracy.
Details
Medium: Buch
Reihe: Texts in Computer Science
Inhalt: xxv
275 S.
64 s/w Illustr.
108 farbige Illustr.
275 p. 172 illus.
108 illus. in color.
ISBN-13: 9783030791032
ISBN-10: 3030791033
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Berry, Stuart
Hill, Richard
Auflage: 1st ed. 2021
Hersteller: Springer International Publishing
Texts in Computer Science
Maße: 241 x 160 x 22 mm
Von/Mit: Stuart Berry (u. a.)
Erscheinungsdatum: 28.09.2021
Gewicht: 0,617 kg
Artikel-ID: 120095867
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