Infopaket

Data mining

ОК.27

Обов’язкова дисципліна для ОП

First

2021/2022

5 Semester

5

– Know the basics of intelligent data analysis. – Know the methods of using educational information. – Know classification and forecasting methods. – Know methods for finding data patterns. – Know the basic concepts of OLAP and Data Mining. – Be able to perform descriptive statistics using the R statistical analysis system and test statistical hypotheses. – Be able to solve problems of regression recovery and construction of decision trees. Be able to apply the method of support vector. – Be able to solve problems related to Data Mining. Using the boosting algorithm in classification tasks. Studying the sample using the bootstrap function. – The ability to work in a team, the development of students' practical skills of group work with the use of appropriate methods and techniques for obtaining, storing and processing data and their presentation with modern technical capabilities.

Full-time form

1) successful mastery of the disciplines "Higher Mathematics", "Theory of Probability and Mathematical Statistics", "Fundamentals of Programming". 2) knowledge of the theoretical foundations of programming languages in order to use them for the possibility of performing laboratory work. 3) possession of elementary skills in mathematical statistics and the basics of algorithmization and programming.

The main task of studying the discipline is to ensure students' understanding and assimilation of the technologies of intellectual data analysis, preparation for their selection, implementation and use in solving applied problems, familiarization with the state and prospects of the development of intellectual methods of calculations as one of the directions of artificial intelligence. After mastering the material of the academic discipline, the student will acquire knowledge about the basic concepts and definitions of intellectual data analysis; methods of using educational information, the method of multidimensional intelligence analysis, methods of classification and forecasting, methods of finding data templates, OLAP and Data Mining, criteria for comparing models and methods of intelligent data analysis, modern software tools for designing and developing intelligent data analysis systems. Perform data preprocessing based on consolidation, transformation, visualization and normalization technologies, analyze and justify the choice of a specific method of intelligent data analysis when solving practical problems, analyze and interpret the results of use for building systems of intelligent data analysis when solving applied problems. The purpose of the discipline is theoretical and practical training of students in the direction of solving problems of processing large arrays of information, designing information support of information systems and developing scenarios of possible actions in conditions of uncertainty using intelligent computing methods.

– Yanchang Zhao. R and Data Mining: Examples and Case Studies. ISBN 978-0-12-396963-7, December 2012. Academic Press, Elsevier. 256 pages. URL: http://www.rdatamining.com/docs/RDataMining-book.pdf – Yanchang Zhao and Yonghua Cen (Eds.). Data Mining Applications with R. ISBN 978-0124115118, December 2013. Academic Press, Elsevier

Lectures, practical activities, laboratory activities, individual work.

The grade for the semester is formed through the successful completion and mandatory defense of laboratory work and course work. As well as, individual research and abstract presentations in practical classes and answers to blitz quizzes in lecture classes. A student receives 1 point for 1 addition or answer to a question during a blitz quiz. There can be 2 such answers. An abstract report in a practical lesson can be evaluated from 3 to 4 points. There are only 8 laboratory works from 1 to 3 points each. MKR1 is written after topic 2 and is evaluated up to 10 points. MKR2 is written after topic 5 and is evaluated up to 10 points. The course work is evaluated according to the 100-point system, and is transferred to the overall semester evaluation with a coefficient of 0.1 and rounded in favor of the student. The total number of points earned during the semester does not exceed 60 points. Final assessment in the form of an exam: is 40 module points (40% from the overall rating).

Ukrainian