Описание: 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.
Автор: Teresa Torres, Torres Название: Continuous discovery habits ISBN: 1736633309 ISBN-13(EAN): 9781736633304 Издательство: Неизвестно Рейтинг: Цена: 2757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: These early stories by the award-winning novelist, David Allan Cates, take place in Mexico and Central America during the war years of the 1980s. The protagonists are exiled lovers-broken for the most part and trying to make sense of their new world of grief. Far from home and working in a boatyard, on a movie set, a banana freighter, as a veal salesman, medical interpreter, writer, and Sandinista volunteer, they`re forced to re-imagine not only love, peace, suffering, and beauty but the meaning of their very own lives. "The stories in David Allan Cates`s Imagining Tanya, sometimes harrowing, sometimes hilarious, are always moving. Like his novels, they`re the perfect mixture of tough and tender, full of heart, mystery, and wisdom. But the compressed form allows him to focus on the strange, subtle moments that turn a life upside down or right it again, even if his characters don`t always recognize the change when it comes. In these rich pages, a host of American ex-pats wandering in Central America-some innocent, some jaded-all carry with them the potential for an earthly, messy sort of grace, gifted to them by a masterful storyteller." - Scott Nadelson, author of One of Us and The Next Scott Nadelson "David Allan Cates creates a vivid, unforgettable world of souls lost in Central America in the 1980s. The characters` heartbreak and displacement are mirrored by the larger conflicts of war all around them, and they seek redemption in the bravery of loving through pain. Imagining Tanya is a bold, gripping, and seductive collection, full of moments of grace." - Maxim Loskutoff, author of Ruthie Fear and Come West and See
Автор: Kudyba, Stephan Название: Big Data, Mining, and Analytics ISBN: 0367378817 ISBN-13(EAN): 9780367378813 Издательство: Taylor&Francis Рейтинг: Цена: 9033.00 р. Наличие на складе: Нет в наличии.
Описание:
There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data resources, including big data, to improve decision making.
Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining.
Includes a foreword by Thomas H. Davenport, Distinguished Professor, Babson College; Fellow, MIT Center for Digital Business; and Co-Founder, International Institute for Analytics
Introduces text mining and the transforming of unstructured data into useful information
Examines real time wireless medical data acquisition for today's healthcare and data mining challenges
Presents the contributions of big data experts from academia and industry, including SAS
Highlights the most exciting emerging technologies for big data
Filled with examples that illustrate the value of analytics throughout, the book outlines a conceptual framework for data modeling that can help you immediately improve your own analytics and decision-making processes. It also provides in-depth coverage of analyzing unstructured data with text mining methods.
Автор: P K Kapur; Ompal Singh Название: Strategic System Assurance and Business Analytics ISBN: 9811536465 ISBN-13(EAN): 9789811536465 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It includes chapters on system performance management, software reliability assessment, testing, quality management, analysis using soft computing techniques, management analytics, and business analytics, with a clear focus on exploring real-world business issues.
Автор: Murugan Anandarajan; Chelsey Hill; Thomas Nolan Название: Practical Text Analytics ISBN: 3319956620 ISBN-13(EAN): 9783319956626 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.
Автор: C. Keith Harrison, Scott Bukstein Название: Sport Business Analytics ISBN: 1498761267 ISBN-13(EAN): 9781498761260 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Нет в наличии.
Описание:
Developing and implementing a systematic analytics strategy can result in a sustainable competitive advantage within the sport business industry. This timely and relevant book provides practical strategies to collect data and then convert that data into meaningful, value-added information and actionable insights. Its primary objective is to help sport business organizations utilize data-driven decision-making to generate optimal revenue from such areas as ticket sales and corporate partnerships. To that end, the book includes in-depth case studies from such leading sports organizations as the Orlando Magic, Tampa Bay Buccaneers, Duke University, and the Aspire Group.
The core purpose of sport business analytics is to convert raw data into information that enables sport business professionals to make strategic business decisions that result in improved company financial performance and a measurable and sustainable competitive advantage. Readers will learn about the role of big data and analytics in:
Ticket pricing
Season ticket member retention
Fan engagement
Sponsorship valuation
Customer relationship management
Digital marketing
Market research
Data visualization.
This book examines changes in the ticketing marketplace and spotlights innovative ticketing strategies used in various sport organizations. It shows how to engage fans with social media and digital analytics, presents techniques to analyze engagement and marketing strategies, and explains how to utilize analytics to leverage fan engagement to enhance revenue for sport organizations.
Filled with insightful case studies, this book benefits both sports business professionals and students. The concluding chapter on teaching sport analytics further enhances its value to academics.
Автор: Finger Lutz, Dutta Soumitra Название: Data Mining: Mining Social Media Data to Build a Better Business ISBN: 1449336752 ISBN-13(EAN): 9781449336752 Издательство: Wiley Рейтинг: Цена: 3800.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This non-technical guide shows you how to extract significant business value from big data with Ask-Measure-Learn, a system that helps you ask the right questions, measure the right data, and then learn from the results.
Автор: Pantea Keikhosrokiani Название: Consumer Behavior Change and Data Analytics in the Socio-Digital Era ISBN: 1668441683 ISBN-13(EAN): 9781668441688 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 38669.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Focuses on the concepts, theories, and analytical techniques to track consumer behaviour change. The book presents multidisciplinary research and practice focusing on social and behavioral analytics to track consumer behaviour shifts and improve decision making among businesses.
Описание: The emergence of new technologies within the industrial revolution has transformed businesses to a new socio-digital era. In this new era, businesses are concerned with collecting data on customer needs, behaviors, and preferences for driving effective customer engagement and product development, as well as for crucial decision making. However, the ever-shifting behaviors of consumers provide many challenges for businesses to pinpoint the wants and needs of their audience.
Consumer Behavior Change and Data Analytics in the Socio-Digital Era focuses on the concepts, theories, and analytical techniques to track consumer behavior change. It provides multidisciplinary research and practice focusing on social and behavioral analytics to track consumer behavior shifts and improve decision making among businesses. Covering topics such as consumer sentiment analysis, emotional intelligence, and online purchase decision making, this premier reference source is a timely resource for business executives, entrepreneurs, data analysts, marketers, advertisers, government officials, social media professionals, libraries, students and educators of higher education, researchers, and academicians.
Описание: Data science has a huge impact on how companies conduct business, and those who don`t learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers.
Автор: Katarzyna Tarnowska; Zbigniew W. Ras; Lynn Daniel Название: Recommender System for Improving Customer Loyalty ISBN: 3030134377 ISBN-13(EAN): 9783030134372 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the Recommender System for Improving Customer Loyalty. New and innovative products have begun appearing from a wide variety of countries, which has increased the need to improve the customer experience. When a customer spends hundreds of thousands of dollars on a piece of equipment, keeping it running efficiently is critical to achieving the desired return on investment. Moreover, managers have discovered that delivering a better customer experience pays off in a number of ways. A study of publicly traded companies conducted by Watermark Consulting found that from 2007 to 2013, companies with a better customer service generated a total return to shareholders that was 26 points higher than the S&P 500. This is only one of many studies that illustrate the measurable value of providing a better service experience.
The Recommender System presented here addresses several important issues. (1) It provides a decision framework to help managers determine which actions are likely to have the greatest impact on the Net Promoter Score. (2) The results are based on multiple clients. The data mining techniques employed in the Recommender System allow users to “learn” from the experiences of others, without sharing proprietary information. This dramatically enhances the power of the system. (3) It supplements traditional text mining options. Text mining can be used to identify the frequency with which topics are mentioned, and the sentiment associated with a given topic. The Recommender System allows users to view specific, anonymous comments associated with actual customers. Studying these comments can provide highly accurate insights into the steps that can be taken to improve the customer experience. (4) Lastly, the system provides a sensitivity analysis feature. In some cases, certain actions can be more easily implemented than others. The Recommender System allows managers to “weigh” these actions and determine which ones would have a greater impact.
Автор: Hofmann Markus Название: RapidMiner ISBN: 1482205491 ISBN-13(EAN): 9781482205497 Издательство: Taylor&Francis Рейтинг: Цена: 12707.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in today’s world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com. Understand Each Stage of the Data Mining ProcessThe book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.
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