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Data Analytics [electronic resource] : Models and Algorithms for Intelligent Data Analysis / by Thomas A. Runkler.

By: Contributor(s): Material type: TextTextPublisher: Wiesbaden : Vieweg+Teubner Verlag : Imprint: Vieweg+Teubner Verlag, 2012Description: X, 137 p. 66 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783834825896
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.312 23
LOC classification:
  • QA76.9.D343
Online resources: In: Springer eBooksSummary: This book is a comprehensive introduction to the methods and algorithms and approaches 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 numerous courses at the Technical University of Munich, 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. Content Data Analytics - Data and Relations - Data Preprocessing - Data Visualization - Correlation - Regression - Forecasting - Classification - Clustering Target Groups Students of data analytics for engineering, computer science and math  Practitioners working on data analytics projects The Author Thomas Runkler is doing research at Siemens Corporate Technology in Munich and teaching data analytics and machine learning at the Technical University of Munich.
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This book is a comprehensive introduction to the methods and algorithms and approaches 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 numerous courses at the Technical University of Munich, 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. Content Data Analytics - Data and Relations - Data Preprocessing - Data Visualization - Correlation - Regression - Forecasting - Classification - Clustering Target Groups Students of data analytics for engineering, computer science and math  Practitioners working on data analytics projects The Author Thomas Runkler is doing research at Siemens Corporate Technology in Munich and teaching data analytics and machine learning at the Technical University of Munich.

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