Guide to industrial analytics solving data science problems for manufacturing and the internet of things / [electronic resource] :
- 作者： Hill, Richard.
- 其他作者：
- 其他題名：
- Texts in computer science.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Texts in computer science,
- 主題： Manufacturing processes--Data processing. , Manufacturing processes--Mathematical models. , Internet of things. , Data Mining and Knowledge Discovery. , Big Data. , Manufacturing, Machines, Tools, Processes. , Machine Learning. , Computer Communication Networks. , Simulation and Modeling.
- ISBN： 9783030791049 (electronic bk.) 、 9783030791032 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: 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.
- 摘要註: 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
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讀者標籤：
- 系統號： 005539033 | 機讀編目格式
館藏資訊
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.