Data Mining basics

This course introduces students to Data Mining technology, examines in detail the methods, tools, and application of Data Mining. A description of each method is accompanied by a specific example of its use.

The differences between Data Mining and classical statistical methods of analysis and OLAP systems are discussed, and the types of patterns revealed by Data Mining (association, classification, sequence, clustering, forecasting) are examined.

Data Mining technology, examines in detail the methods.
This course introduces students to Data Mining technology, examines in detail the methods, tools, and application of Data Mining.

The scope of Data Mining is described. The concept of Web mining is introduced. The Data Mining methods are considered in detail: neural networks, decision trees, limited enumeration methods, genetic algorithms, evolutionary programming, cluster models, combined methods. Familiarity with each method is illustrated by solving a practical problem with the help of instrumental.

Tools that use Data Mining technology.

The basic concepts of data warehouses and the place of Data Mining in their architecture are described. The concepts of OLTP, OLAP, ROLAP, MOLAP, Orange software are introduced.

The process of data analysis using Data Mining technology is discussed. The stages of this process are considered in detail. The analyzed market of analytical software describes products from leading Data Mining manufacturers, discussing their capabilities.

Purpose To acquaint students with the theoretical aspects of Data Mining technology, methods, the possibility of their application, to give practical skills in using the Data Mining tools.

Prior knowledge

Knowledge of computer science, the basics of database theory, knowledge of mathematics (within the initial courses of a university), and information processing technology is desirable, but not required.

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