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© 2016

Data Analytics

Models and Algorithms for Intelligent Data Analysis

Textbook

Table of contents

  1. Front Matter
    Pages i-xii
  2. Thomas A. Runkler
    Pages 1-3
  3. Thomas A. Runkler
    Pages 5-22
  4. Thomas A. Runkler
    Pages 23-36
  5. Thomas A. Runkler
    Pages 37-58
  6. Thomas A. Runkler
    Pages 59-65
  7. Thomas A. Runkler
    Pages 67-83
  8. Thomas A. Runkler
    Pages 85-89
  9. Thomas A. Runkler
    Pages 91-109
  10. Thomas A. Runkler
    Pages 111-132
  11. Back Matter
    Pages 133-150

About this book

Introduction

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.


Content

• Data Analytics

• Data and Relations

• Data Preprocessing

• Data Visualization

• Correlation

• Regression

• Forecasting

• Classification

• Clustering


Target Groups

  • Students of computer science, mathematics and engineering
  • Data analytics practitioners


The Author

Thomas A. Runkler is Principal Research Scientist at Siemens Corporate Technology and Professor for Computer Science at the Technical University of Munich.


Keywords

data mining knowledge discovery algorithms forecasting classification clustering business intelligence machine learning deep learning

Authors and affiliations

  1. 1.MünchenGermany

About the authors

Thomas A. Runkler is Principal Research Scientist at Siemens Corporate Technology and Professor for Computer Science at the Technical University of Munich.

Bibliographic information

Industry Sectors
Pharma
Automotive
Biotechnology
IT & Software
Telecommunications
Law
Consumer Packaged Goods
Finance, Business & Banking
Electronics
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering

Reviews

“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)