Guide to Industrial Analytics Solving Data Science Problems for Manufacturing and the Internet of Things 1st Edition by Richard Hill 9783030791049 3030791041

Guide to Industrial Analytics Solving Data Science Problems for Manufacturing and the Internet of Things 1st Edition Richard Hill – Ebook Instant Download/Delivery ISBN(s): 9783030791032, 3030791033, 9783030791049, 3030791041

Product details:

• ISBN 10: 3030791041
• ISBN 13: 9783030791049
• Author: Richard Hill

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.

Table contents:

Part I. Introductory Concepts
1. An Introduction to Industrial Analytics
2. Data, Analysis and Statistics
3. Measuring Operations
4. Data for Production Planning and Control
Part II. Methods
5. Simulating Industrial Processes
6. From Process to System Simulation
7. Constructing Machine Learning Models for Prediction
Part III. Application
8. Case Study: Confectionery Production
9. Minimum Information Set for Effective Control
10. Business Adoption of Analytics

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