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Introduction,refully distinguish between “data” and “knowledge” in order to obtain clear notions that help us to work out why it is usually not enough to simply collect data and why we have to strive to turn them into knowledge. As an illustration, we consider a well-known example from the history of science. In

Chagrin

BIAS

narcotic

Data Understanding,ysis process, but data understanding should not be driven exclusively by the goals and methods to be applied in later steps. Although these requirements should be kept in mind during data understanding, one should approach the data from a neutral point of view. Never trust any data as long as you ha

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WAG

Data Preparation,ations. We intend to apply various modeling techniques to extract models from the data. Although we have not yet discussed any modeling technique in greater detail (see Chaps. 7ff), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we star

TEM

arterioles

Finding Explanations, in order to group similar objects. In this chapter we will discuss methods that address a very different setup: instead of finding structure in a data set, we are now focusing on methods that find explanations for an unknown dependency within the data. Such a search for a dependency usually focuses

Conjuction

Finding Predictors,e discussed methods for basically the same purpose, the methods in this chapter yield models that do not help much to explain the data or even dispense with models altogether. Nevertheless, they can be useful, namely if the main goal is good prediction accuracy rather than an intuitive and interpret

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