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Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data (Texts in Computer Science)

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The main motivation to write this book came from all our problems to find suitable material for a textbook that would really help us to teach the practical aspects of data analysis together with the needed theoretical underpinnings. Many books out there tackle either one or the other of these aspects (and, especially for the latter, there are some fantastic text books out there), but a book providing a good combination was nowhere to be found.

The idea to write our own book to address this shortcoming arose in two different places at the same time—when one of the authors was asked to review the book proposal of the others, we quickly realized that it would be much better to join forces instead of independently pursuing our individual projects.

We hope that this book helps others to learn what kind of challenges data analysts face in the real world and at the same time provides them with solid knowledge about the processes, algorithms, and theories to successfully tackle these problems. We have put a lot of effort into balancing the practical aspects of applying and using data analysis techniques while making sure at the same time that we did not forget to also explain the statistical and mathematical underpinnings behind the algorithms beneath all of this.

There are many people to be thanked, and we will not attempt to list them all. However, we do want to single out Iris Adä who has been a tremendous help with the generation of the data sets used in this book. She and Martin Horn also deserve our thanks for an intense last minute round of proof reading.

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