Infopaket

Preliminary data analysis and preparation

ОК 33

Обов’язкова дисципліна для ОП

First

2021/2022

6 Semester

4

The ability to use knowledge of patterns 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. The ability to design, develop and analyze algorithms for solving computational and logical problems, to evaluate the effectiveness and complexity of algorithms based on the application of formal models of algorithms and calculated functions. The ability to develop software models of subject environments, to choose a programming paradigm from the standpoint of convenience and quality of application for the implementation of methods and algorithms for problem solving. Know and apply data analysis tools, cloud computing and artificial intelligence methods for solving applied problems, including time series analysis, statistical problems, determination of probabilistic relationships between data, economic problems.

Full-time form

Knowledge of the basics of statistics, discrete mathematics and mathematical logic, set theory, basic approaches, methods and technologies of algorithmizing and programming. The ability to search for data in the network, to analyze the information space of tasks in order to structure the input and output information needed to solve specific tasks. Possession of elementary work skills in any instrumental programming environment.

The study of the educational discipline «Preliminary data analysis and preparation» is aimed at students' acquisition of competencies in working with application software packages for solving problems of data analysis and interpretation, for creating forecasts and supporting the adoption of management decisions. Within the framework of the discipline, various methods of extraction, data formatting, a description of the basic principles of working with tabular data for the purpose of their further analysis using statistical and mathematical methods, as well as features of visual presentation of data are considered.

1. Khlevna I. PrimaDoc – an enterprise information management system: implementation of the development and deployment project [Текст]/ Khlevna I., Yehorchenkov O., Boyko N., Teslia I., Ivanov Y., Kubiavka L., Latysheva Y., Yehorchenkova N., Kravchuk N.// The 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2017) September 21-23, 2017 Bucharest, Romania, pp. 923-929 2. Iuliia L.Khlevna, Bohdan S. Koval. Development of the automated fraud detection system concept in payment systems. Applied Aspects of Information Technology. Vol. 4 №1 (9). Р. 37 – 46. DOI: 10.15276/aait.01.2021.3. https://aait.opu.ua/?fetch=articles&with=info&id=70 3. Hrytsyuk P.M., Ostapchuk O.P. Data Analysis: A Study Guide. - Rivne: NUVHP, 2008. - 218 p. 4. Horokhovatsky V.O., Tvoroshenko I.S. Methods of intellectual analysis and data processing: Study guide. – Kharkiv: KHNURE, 2021. – 92 p.

Lectures, laboratory activities, individual work.

Assessment of students during the semester for all types of work is carried out. The total score for the semester is formed as a weighted sum of points earned by the student for various types of work. The maximum number of points that a student can receive for work in a semester does not exceed 100 points. The form of the final evaluation is the credit. The assessment is carried out by issuing a final grade, which is defined as the sum of points for all successfully assessed learning outcomes. To receive credit, it is mandatory to complete all laboratory work (minimum grade – 50 points, maximum – 75 points), individual work (minimum grade – 5 points, maximum – 10 points), and drawing up the final test control work (minimum grade – 5 points, maximum – 15 points). Upon receiving the resulting final number of points from 60 and above, the student is assigned a credit.

Ukrainian