Data Analytics
Models and Algorithms for Intelligent Data Analysis
2nd Edition
Published on 3. August 2016
Book
Paperback/Softback
XII, 150 pages
978-3-658-14074-8 (ISBN)
Description
This book is a comprehensive introduction to the methods and algorithms of modern data analytics. 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. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.
Reviews / Votes
"The book is especially suitable as a course support (especially as each chapter also ends with a set of exercises), but also as introductory material for master or PhD students that are new to the data analytics domain, as they can obtain a quick overview." (Ruxandra Stoean, zbMATH 1373.68007, 2017)More details
Edition
2nd ed. 2016
Language
English
Place of publication
Wiesbaden
Germany
Publishing group
Springer Fachmedien Wiesbaden GmbH
Target group
Professional and scholarly
Edition type
Revised edition
Illustrations
66 s/w Abbildungen
66 Illustrations, black and white; XII, 150 p. 66 illus.
Dimensions
Height: 24 cm
Width: 16.8 cm
Weight
289 gr
ISBN-13
978-3-658-14074-8 (9783658140748)
DOI
10.1007/978-3-658-14075-5
Schweitzer Classification
Other editions
New editions
Thomas A. Runkler
Data Analytics
Models and Algorithms for Intelligent Data Analysis - A Comprehensive Introduction
Book
11/2024
4th Edition
Springer Vieweg
€37.44
Shipment within 10-15 days
Book
05/2020
3rd Edition
Springer Vieweg
€53.49
Article exhausted; check for reprint
Additional editions
E-Book
07/2016
2nd Edition
Springer Vieweg
€39.58
Available for download
Previous edition
Book
09/2012
Vieweg+Teubner Verlag
€26.70
Article exhausted; check for reprint
Person
Thomas A. Runkler is Principal Research Scientist at Siemens Corporate Technology and Professor for Computer Science at the Technical University of Munich.
Content
Data Analytics - Data and Relations - Data Preprocessing - Data Visualization - Correlation - Regression - Forecasting - Classification - Clustering.