Data Analytics Applied To The Mining Industry

DOWNLOAD

Download Data Analytics Applied To The Mining Industry PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Data Analytics Applied To The Mining Industry book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Data Analytics Applied To The Mining Industry

DOWNLOAD
Author : Ali Soofastaei
language : en
Publisher: CRC Press
Release Date : 2020-11-12

Data Analytics Applied To The Mining Industry written by Ali Soofastaei and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-12 with Computers categories.

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors

Advanced Analytics In Mining Engineering

DOWNLOAD
Author : Ali Soofastaei
language : en
Publisher: Springer Nature
Release Date : 2022-02-23

Advanced Analytics In Mining Engineering written by Ali Soofastaei and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-23 with Business & Economics categories.

In this book, Dr. Soofastaei and his colleagues reveal how all mining managers can effectively deploy advanced analytics in their day-to-day operations- one business decision at a time. Most mining companies have a massive amount of data at their disposal. However, they cannot use the stored data in any meaningful way. The powerful new business tool-advanced analytics enables many mining companies to aggressively leverage their data in key business decisions and processes with impressive results. From statistical analysis to machine learning and artificial intelligence, the authors show how many analytical tools can improve decisions about everything in the mine value chain, from exploration to marketing. Combining the science of advanced analytics with the mining industrial business solutions, introduce the “Advanced Analytics in Mining Engineering Book” as a practical road map and tools for unleashing the potential buried in your company’s data. The book is aimed at providing mining executives, managers, and research and development teams with an understanding of the business value and applicability of different analytic approaches and helping data analytics leads by giving them a business framework in which to assess the value, cost, and risk of potential analytical solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate IT and mining engineering students – with an understanding of data analytics applied to the mining industry. By providing a book with chapters structured in line with the mining value chain, we will provide a clear, enterprise-level view of where and how advanced data analytics can best be applied. This book highlights the potential to interconnect activities in the mining enterprise better. Furthermore, the book explores the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of creating a digital mine with much-enhanced capabilities for modeling, simulation, and the use of digital twins – in line with leading “digital” industries.

Advanced Analytics For Industry 4 0

DOWNLOAD
Author : Ali Soofastaei
language : en
Publisher: CRC Press
Release Date : 2025-07-17

Advanced Analytics For Industry 4 0 written by Ali Soofastaei and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-17 with Computers categories.

The evolution of modern technology has affected all the industry dimensions. Mother industries play a critical role in providing the precursor materials for other industries, and a small improvement in these can make a big change in others. This book covers the analytics revolution in Industry 4.0 for the mother industries, such as mining, oil and gas, and steel. It focuses on the use of advanced analytics and artificial intelligence to improve the business decisions aimed at increasing the quality and quantity of mother industries' products. It helps to design and implement their digital transformation strategies in these industries. Key Features: Provides a concise overview of state of the art for mother industries' executives and managers. Highlights and describes critical opportunity areas for industry operations optimization. Explains how to implement advanced data analytics through case studies and examples. Provides approaches and methods to improve data-driven decision-making. Brings experience and learning in digital transformation from adjacent sectors. This book is aimed at researchers, professionals, and graduate students in data science, manufacturing, automation, and computer engineering.

Real World Data Mining

DOWNLOAD
Author : Dursun Delen
language : en
Publisher:
Release Date : 2014

Real World Data Mining written by Dursun Delen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Big data categories.

Annotation Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your organization! Real-World Data Mining demystifies current best practices, showing how to use data mining to uncover hidden patterns and correlations, and leverage these to improve all aspects of business performance.Drawing on extensive experience as a researcher, practitioner, and instructor, Dr. Dursun Delen delivers an optimal balance of concepts, techniques and applications. Without compromising either simplicity or clarity, he provides enough technical depth to help readers truly understand how data mining technologies work. Coverage includes: processes, methods, techniques, tools, and metrics; the role and management of data; text and web mining; sentiment analysis; and Big Data integration. Throughout, Delen's conceptual coverage is complemented with application case studies (examples of both successes and failures), as well as simple, hands-on tutorials.Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field; and undergraduate or graduate students in any analytics program: concentrations, certificate-based, or degree-based.

Data Mining Approaches For Big Data And Sentiment Analysis In Social Media

DOWNLOAD
Author : Brij Gupta
language : en
Publisher:
Release Date : 2021

Data Mining Approaches For Big Data And Sentiment Analysis In Social Media written by Brij Gupta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Big data categories.

"This book explores the key concepts of data mining and utilizing them on online social media platforms, offering valuable insight into data mining approaches for big data and sentiment analysis in online social media and covering many important security and other aspects and current trends"--

Mining Your Own Business

DOWNLOAD
Author : Jeff Deal
language : en
Publisher:
Release Date : 2016-09-19

Mining Your Own Business written by Jeff Deal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-19 with categories.

Practical guide for organization leaders, top-level executives. Industry experts explain in clear, understandable English. What data mining and predictive analytics are

Data Mining In Agriculture

DOWNLOAD
Author : Antonio Mucherino
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-09-22

Data Mining In Agriculture written by Antonio Mucherino and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-22 with Mathematics categories.

Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.

Data Mining And Business Analytics With R

DOWNLOAD
Author : Johannes Ledolter
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-28

Data Mining And Business Analytics With R written by Johannes Ledolter and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-28 with Mathematics categories.

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Data Mining For Business Analytics

DOWNLOAD
Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2019-10-14

Data Mining For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Mathematics categories.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R