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Data Analytics pp 1–3Cite as

Introduction

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Abstract

This book deals with models and algorithms for the analysis of data sets, for example industrial process data, business data, text and structured data, image data, and biomedical data. We define the terms data analytics, data mining, knowledge discovery, and the KDD and CRISP-DM processes. Typical data analysis projects can be divided into several phases: preparation, preprocessing, analysis, and postprocessing. The chapters of this book are structured according to the main methods of data preprocessing and data analysis: data and relations, data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering.

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Authors and Affiliations

  1. Siemens AG, München, Germany

    Thomas A. Runkler

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  1. Thomas A. Runkler
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Correspondence to Thomas A. Runkler .

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© 2016 Springer Fachmedien Wiesbaden

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Runkler, T. (2016). Introduction. In: Data Analytics. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-14075-5_1

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  • DOI: https://doi.org/10.1007/978-3-658-14075-5_1

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  • Publisher Name: Springer Vieweg, Wiesbaden

  • Print ISBN: 978-3-658-14074-8

  • Online ISBN: 978-3-658-14075-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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