Abstract
Introduction
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.
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.
| Original language | English |
|---|---|
| Place of Publication | Cham |
| Publisher | Springer Nature Switzerland AG |
| Number of pages | 275 |
| Edition | 1 |
| ISBN (Electronic) | 9783030791049 |
| ISBN (Print) | 9783030791032, 9783030791063 |
| DOIs | |
| Publication status | Published - 28 Sep 2021 |
Publication series
| Name | Texts in Computer Science |
|---|---|
| Publisher | Springer |
| ISSN (Print) | 1868-0941 |
| ISSN (Electronic) | 1868-095X |
