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Data Science is a rapidly expanding field with increasing relevance. There are correspondingly numerous textbooks about the topic. They usually focus on various Data Science methods. In a growing field, there is a danger that the number of methods grows, too, in a pace that it is difficult to compare their specific merits and application focus.

To cope with this method avalanche, the user is left alone with the judgment about the method selection. He or she can be helped only if some basic principles such as fitting model to data, generalization, and abilities of numerical algorithms are thoroughly explained, independently from the methodical approach. Unfortunately, these principles are hardly covered in the textbook variety. This book would like to close this gap.