Ill this thesis neuro-fuzzy methods for data analysis are discussed. We consider data analysis as a process that is exploratory to some extent. If a fuzzy model is to be created in a data analysis process it is important to have learning algorithms available that support this exploratory nature. This thesis systematically presents such learning algorithms, which can be used to create fuzzy systems from data. The algorithms are especially designed for their capability to produce interpretable fuzzy systems. It is important that during learning the main advantages of a fuzzy system - its simplicity and interpretability - do not get lost. The algorithms are presented in such a way that they can readily be used for implementations. As an example for neuro-fuzzv data analvsis the classification svstem NEFCLASS is discussed.