This is an unconventional course in modern Data Analysis and Mining. Its contents are heavily influenced by the idea that data analysis should help in enhancing and augmenting knowledge of the domain as represented by the concepts and statements of relation between them. According to this view, two main pathways for data analysis are summarization, for developing and augmenting concepts, and correlation, for enhancing and establishing relations. Visualization, in this context, is a way of presenting results in a cognitively comfortable way. The term summarization is understood quite broadly here to embrace not only simple summaries like totals and means, but also more complex summaries: the principal component of a set of features and cluster structures in a set of entities. Similarly, correlation here covers both bivariate and multivariate relations between input and target features including classification trees and Bayes classifiers. The course topics: 1D analysis (histograms, centrality and spread values, bootstrap), 2D analysis (scatter plot and linear regression, contingency table and chi-squared visualized), naïve Bayes classifier and decision trees, principal component for scoring a hidden factor, k-means and related methods for clustering.