Basic concepts and overview of the field of DM. Exploratory data analysis and visualization. Basic techniques of classification: decision trees, naive Bayes method, k-nearest neighbors and support vector machines. Advanced classification techniques: the classifier ensembles, bagging, boosting, semi-supervised learning. Classifier evaluation, automatic determination of the parameter values ??and selection of attributes. Clustering techniques: k-means, hierarchical clustering, dbscan algorithm. Discovering association rules: apriori and FP-growth algorithm. Review of the application of data mining: analysis of business data, web data analysis, recommendation systems (films, books, etc.), predictions in sport.