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Foundations of Predictive Analytics, Wu, James


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Автор: Wu, James
Название:  Foundations of Predictive Analytics
ISBN: 9781439869468
Издательство: Taylor&Francis
Классификация:
ISBN-10: 1439869464
Обложка/Формат: Hardback
Страницы: 338
Вес: 0.59 кг.
Дата издания: 15.02.2012
Язык: English
Размер: 166 x 250 x 21
Читательская аудитория: Postgraduate, research & scholarly
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Поставляется из: Европейский союз


Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database

Автор: Jones Herbert
Название: Data Science for Business: Predictive Modeling, Data Mining, Data Analytics, Data Warehousing, Data Visualization, Regression Analysis, Database
ISBN: 1647483263 ISBN-13(EAN): 9781647483265
Издательство: Неизвестно
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Цена: 4137.00 р.
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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.

Statistical Data Analytics -  Foundations for Data  Mining, Informatics, and Knowledge Discovery

Автор: Piegorsch
Название: Statistical Data Analytics - Foundations for Data Mining, Informatics, and Knowledge Discovery
ISBN: 111861965X ISBN-13(EAN): 9781118619650
Издательство: Wiley
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Цена: 12981.00 р.
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Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery

Автор: Walter W. Piegorsch
Название: Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery
ISBN: 111903065X ISBN-13(EAN): 9781119030652
Издательство: Wiley
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Цена: 2685.00 р.
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Описание: Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery.

Commercial Data Mining

Автор: David Nettleton
Название: Commercial Data Mining
ISBN: 0124166024 ISBN-13(EAN): 9780124166028
Издательство: Elsevier Science
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Цена: 6230.00 р.
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Описание: Helps you learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. This book guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.

Disruptive Business Analytics

Автор: Dinsmore Thomas W.
Название: Disruptive Business Analytics
ISBN: 1484213122 ISBN-13(EAN): 9781484213124
Издательство: Springer
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Цена: 3492.00 р.
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Описание: Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities.Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization.Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today.What You'll LearnDiscover how the open source business model works and how to make it work for youSee how cloud computing completely changes the economics of analyticsHarness the power of Hadoop and its ecosystemFind out why Apache Spark is everywhereDiscover the potential of streaming and real-time analyticsLearn what Deep Learning can do and why it mattersSee how self-service analytics can change the way organizations do business

Who This Book Is For
Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.
Foundations of predictive analytics

Автор: Wu, James Coggeshall, Stephen
Название: Foundations of predictive analytics
ISBN: 0367381680 ISBN-13(EAN): 9780367381684
Издательство: Taylor&Francis
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Цена: 9492.00 р.
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Описание:

Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts.





The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish-Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naпve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster-Shafer theory.





An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference.



Web Resource
The book's website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.

Systems 4.0

Автор: Badiru, Adedeji B.
Название: Systems 4.0
ISBN: 1032319186 ISBN-13(EAN): 9781032319186
Издательство: Taylor&Francis
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Цена: 8420.00 р.
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IBM SPSS Modeler Essentials

Автор: Salcedo Jesus, McCormick Keith
Название: IBM SPSS Modeler Essentials
ISBN: 1788291115 ISBN-13(EAN): 9781788291118
Издательство: Неизвестно
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Цена: 7171.00 р.
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Описание: IBM SPSS Modeler allows quick, efficient predictive analytics and insight building from your data, and is a popularly used data mining tool. This book will guide you through the data mining process, and presents relevant statistical methods which are used to build predictive models and conduct other analytic tasks using IBM SPSS Modeler. From ...

Predictive safety analytics

Автор: Stevens, Robert
Название: Predictive safety analytics
ISBN: 1032424389 ISBN-13(EAN): 9781032424385
Издательство: Taylor&Francis
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Цена: 8420.00 р.
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Описание: Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense.Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where organizations have reduced incidents by employing predictive analytics to foresee and mitigate future risks. It discusses how Predictive Safety Analytics is an opportunity to break through the plateau problem where safety rate improvements have stagnated in many organizations. The book presents how the use of data, coupled with advanced analytical techniques, including machine learning, has become a proven and successful innovation. Emphasis is placed on how the book can “meet you where you are” by illuminating a path to get there, starting with simple data the organization likely already has. Highlights of the book are the real examples and case studies that will assist in generating thoughts and ideas for what might work for individual readers and how they can adapt the information to their particular situations.This book is written for professionals and researchers in system reliability, risk and safety assessment, quality control, operational managers in selected industries, data scientists, and ML engineers. Students taking courses in these areas will also find this book of interest to them.

Data Mining and Predictive Analytics

Автор: Larose Daniel T
Название: Data Mining and Predictive Analytics
ISBN: 1118116194 ISBN-13(EAN): 9781118116197
Издательство: Wiley
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Цена: 18842.00 р.
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Описание: Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis.

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
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Цена: 6178.00 р.
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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.

Demand Prediction in Retail

Автор: Cohen, Maxime C. Gras, Paul-Emile Pentecoste, Arthur Zhang, Renyu
Название: Demand Prediction in Retail
ISBN: 303085857X ISBN-13(EAN): 9783030858575
Издательство: Springer
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Цена: 9083.00 р.
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Описание: From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers.


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