In-depth exploration, investigation and analysis of data sets as well as appropriate preprocessing for advanced processing such as machine learning, prediction and forecasting. This may include outlier identification, data cleaning, distribution and density estimation, correlation analysis, dimension reduction, regression, clustering, detection of association rules, independent component analysis, data augmentation and related methods. Of course results and intermediate steps are visualized properly for the purpose of validation and presentation. This process may reveal previously unknown aspects of the data and find precious patterns which indicate potential possibilities of learning, prediction and optimization.