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Data Analytics : Models and Algorithms for Intelligent Data Analysis / by Thomas A Runkler

By: Runkler, Thomas A.
Contributor(s): SpringerLink (Online service)
Material type: materialTypeLabelE-bookPublisher: Wiesbaden : Springer Fachmedien Wiesbaden Vieweg, 2016Edition: 2nd ed.Description: 1 recurso en línea (XII, 150 p.) : 66 ilustraciones.ISBN: 9783658140755.Subject: Data mining | Estructuras de datos (Informática)DDC classification: 006.312 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Data Analytics -- Data and Relations -- Data Preprocessing -- Data Visualization -- Correlation -- Regression -- Forecasting -- Classification -- Clustering.
Abstract: This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. The text is designed for undergraduate and graduate courses on data analytics for engineering, computer science, and math students. It is also suitable for practitioners working on data analytics projects. This book has been used for more than ten years in numerous courses at the Technical University of Munich, Germany, in short courses at several other universities, and in tutorials at scientific conferences. Much of the content is based on the results of industrial research and development projects at Siemens
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería QA76.9.D343 R865 2016 EB (Browse shelf(Opens below)) .i11598748 Acceso electrónico eBOOK .i11598748
Total holds: 0

Data Analytics -- Data and Relations -- Data Preprocessing -- Data Visualization -- Correlation -- Regression -- Forecasting -- Classification -- Clustering.

This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. The text is designed for undergraduate and graduate courses on data analytics for engineering, computer science, and math students. It is also suitable for practitioners working on data analytics projects. This book has been used for more than ten years in numerous courses at the Technical University of Munich, Germany, in short courses at several other universities, and in tutorials at scientific conferences. Much of the content is based on the results of industrial research and development projects at Siemens

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