Data mining

The course introduces students to the principles, processes and main methods of data mining, including data preparation, association analysis, classification, clustering, outlier detection and text mining. Emphasis is placed on selecting and applying appropriate analytical procedures, interpreting results and using software tools to solve practical problems.
Professional knowledge
• The student will explain and critically assess the fundamental principles, terminology and specific features of data mining and its relationships with other scientific disciplines;
• The student will characterise and compare data types, approaches to data representation and the main stages of knowledge discovery from data, including preprocessing, data cleaning and interpretation of results;
• The student will distinguish and evaluate the principles of direct and indirect data mining and explain the foundations of classification, clustering, association, network-analysis and text-mining methods, including the use of computational intelligence.
Professional skills
• The student will analyse the characteristics of data and the requirements of a specific problem and select, justify and apply an appropriate data-mining procedure;
• The student will design and implement a data-processing workflow from data preparation and method selection to interpretation of the obtained results;
• The student will compare and evaluate the results of classical and flexible classification and clustering methods and assess their suitability for solving more complex problems;
• The student will apply and interpret selected methods of association analysis, decision trees, network analysis, statistical and logical data summarisation, opinion aggregation and text mining.
General competences
• The student will make independent and responsible decisions on the choice of an analytical procedure in new or changing conditions and professionally justify the selected approach;
• The student will critically evaluate the quality, limitations and explanatory value of data-mining results and formulate conclusions appropriate to the problem being solved;
• The student will integrate knowledge from logical, statistical and computational-intelligence approaches to data and clearly present and justify the proposed solution.