Infopaket

Data analysis

ВК.4.03.01

Вибіркова дисципліна для ОП

First

2023/2024

5 Semester

3

PRN1. Apply thorough knowledge of the basic forms and laws of abstract and logical thinking, the foundations of the methodology of scientific knowledge, the forms and methods of extracting, analyzing, processing and synthesizing information in the subject area of ​​computer science. PRN3. To use the knowledge of regularities of random phenomena, their properties and operations on them, models of random processes and modern software environments to solve problems of statistical data processing and build predictive models. PRN12. Apply methods and algorithms of computational intelligence and intelligent data analysis in tasks of classification, forecasting, cluster analysis, search for associative rules using software tools to support multidimensional data analysis based on DataMining, TextMining, WebMining technologies.

Know: probability theory and mathematical statistics. Be able to: apply knowledge of probability theory and mathematical statistics. Possess elementary skills: solve problems in probability theory and mathematical statistics.

The discipline has the following sections: pre-processing of data, correlation analysis, regression analysis, dispersion analysis, covariance analysis, time series analysis, classification problems. The main task is to provide students with basic knowledge of the entire arsenal of methods and tools in all the main sections of data analysis and to gain experience in working with relevant software when solving applied problems. Uses concepts from probability theory and mathematical statistics, mathematical analysis and algebra. It acts as a base for the disciplines: intelligent systems, control theory and the basics of robotics, a number of disciplines of the student's free choice (by blocks), and will also be useful when writing bachelor's and master's graduation theses. The discipline is a discipline of the student's free choice.

Main: 1. Afifi A. Statisticheskii analiz. Podkhod s ispol-zovaniem EVM / A. Afifi, S. Eizen. — M.: Mir, 1982. 2. Brandt Z. Analiz dannykh / Z. Brandt. — M.: Mir, 2003. 3. Dreiper N. Prikladnoi regressionnyi analiz / N. Dreiper, G. Smit. — 3-e izdanie. — K.: Dialektika, 2007. 4. Kendall M. Mnogomernyi statisticheskii analiz i vremennye riady / M. Kendall, A. St-iuart. — M.: Nauka, 1976. 5. Prikladna statistika: Osnovy modelirovaniia i pervichnaia obrabotka dannykh / S. A. Aivazian i dr. — M.: Finansy i statistika, 1983. 6. Prikladna statistika: Issledovanie zavisimostei / S. A. Aivazian i dr. — M.: Finansy i statistika, 1985.

Lectures, practical classes, consultations, independent work

- semester assessment: 1. Test papers: RN.1, RN.2, RN.4 - 60 points/36 points. 2. Current assessment: RN.1, RN.2, RN.4 - 40 points/24 points. The maximum number of points that can be obtained by a student: 100. A student is admitted to credit if he has scored at least 60 points in the semester. To receive an overall positive grade in the discipline, the credit score must be at least 60 points. The credit is given based on the results of the student's work throughout the semester and does not include additional assessment measures for successful students.

Ukrainian