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Data Mining for Business Analytics - Concepts, Techniques, and Applications with JMP Pro(R), Shmueli



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Цена: 19219р.
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При оформлении заказа до: 17 фев 2025
Ориентировочная дата поставки: Март
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Автор: Shmueli
Название:  Data Mining for Business Analytics - Concepts, Techniques, and Applications with JMP Pro(R)
ISBN: 9781118877432
Издательство: Wiley
Классификация:
ISBN-10: 1118877438
Обложка/Формат: Hardback
Страницы: 464
Вес: 0.974 кг.
Дата издания: 26.07.2016
Серия: Economics/Business/Finance
Язык: English
Размер: 186 x 262 x 30
Читательская аудитория: Professional & vocational
Ключевые слова: Industrial relations,Mathematics,Computer hardware
Основная тема: Data Mining Statistics
Подзаголовок: Concepts, techniques, and applications with jmp pro
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) presents an applied and interactive approach to data mining.


      Новое издание
Machine learning for business analytics

Автор: Bruce, Peter C. (massachusetts Institute Of Technology) Stephens, Mia L. Shmueli, Galit (university Of Maryland, College Park) Anandamurthy, Muralidha
Название: Machine learning for business analytics
ISBN: 1119903831 ISBN-13(EAN): 9781119903833
Издательство: Wiley
Цена: 18361 р.
Наличие на складе: Поставка под заказ.


Data Science For Business: What You Need To Know About Data Mining And Dataanalytic Thinking

Автор: Foster Provost
Название: Data Science For Business: What You Need To Know About Data Mining And Dataanalytic Thinking
ISBN: 1449361323 ISBN-13(EAN): 9781449361327
Издательство: Wiley
Рейтинг:
Цена: 6862 р.
Наличие на складе: Ожидается поступление.

Описание: This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection

Автор: Baesens Bart, Verbeke Wouter, Van Vlasselaer Veron
Название: Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection
ISBN: 1119133122 ISBN-13(EAN): 9781119133124
Издательство: Wiley
Рейтинг:
Цена: 6692 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution.

Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner

Автор: Galit Shmueli, Peter C. Bruce, Nitin R. Patel
Название: Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
ISBN: 1118729277 ISBN-13(EAN): 9781118729274
Издательство: Wiley
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Цена: 19219 р.
Наличие на складе: Поставка под заказ.

Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.

Data Mining: Concepts and Techniques,

Автор: Jiawei Han
Название: Data Mining: Concepts and Techniques,
ISBN: 0123814790 ISBN-13(EAN): 9780123814791
Издательство: Elsevier Science
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Цена: 10645 р.
Наличие на складе: Нет в наличии.

Описание: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.

Data Mining: Practical Machine Learning Tools and Techniques,

Автор: Ian H. Witten
Название: Data Mining: Practical Machine Learning Tools and Techniques,
ISBN: 0123748569 ISBN-13(EAN): 9780123748560
Издательство: Elsevier Science
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Цена: 9523 р.
Наличие на складе: Нет в наличии.

Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>

Data Mining: Concepts and Techniques

Автор: Han, Jiawei et al.
Название: Data Mining: Concepts and Techniques
ISBN: 1558609016 ISBN-13(EAN): 9781558609013
Издательство: Elsevier Science
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Цена: 5407 р.
Наличие на складе: Невозможна поставка.

Описание: Our ability to generate and collect data has increased. This book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. It includes chapters that address the developments on mining complex types of data.

Data Mining and Business Analytics with R

Автор: Ledolter Johannes
Название: Data Mining and Business Analytics with R
ISBN: 111844714X ISBN-13(EAN): 9781118447147
Издательство: Wiley
Рейтинг:
Цена: 18696 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: 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.


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