Information processing and analysis technologies

Course: Applied mathematics

Structural unit: Faculty of Computer Science and Cybernetics

Title
Information processing and analysis technologies
Code
ДВС.3.03.02
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
3
Learning outcomes
PLO3. Gaining knowledge for the ability to evaluate existing technologies and on the basis of analysis to form requirements for the development of advanced information technologies. PLO6. Be able to design and use existing data integration tools, process data stored in different systems. PLO7. Be able to organize, configure and develop Web-systems using the principles of distributed systems, hypertext systems, appropriate hardware and software. PLO10. Be able to build models of physical and production processes, design of storage and data space, knowledge base, using charting techniques and standards for information systems development.
Form of study
Distance form
Prerequisites and co-requisites
Know: the basics of tabular data manipulation, the concept of relational databases and their transformations, in particular, normalization and denormalization; Be able to: apply in practice instrumental environments of programming and data processing. Have the skills of: visual design.
Course content
The goal of the learning course is to acquire basic knowledge and master the skills of data processing technologies and the development of information and analytical systems. The educational course "Information processing and analysis technologies" is a component of the program of training specialists at the second (master's) level of higher education in the field of knowledge 11 "Mathematics and statistics" from specialty 113 "Applied mathematics", educational and professional program "Applied mathematics". This course is optional. It is taught in the 2nd semester of the 2nd year in the amount of 90 hours. (4 ECTS credits), in particular: lectures - 10 hours, laboratory - 10 hours, consultations - 2 hours, independent work - 68 hours. The course ends with an exam in the 2nd semester.
Recommended or required reading and other learning resources/tools
1. Akimenko V.V. Prykladni zadachi intekektualnoho analizu danykh (DATA MINING). – Кyiv, 2018. – 152 p. 2. Understanding Microsoft OLAP Architecture. – [Електронний ресурс], режим доступу: https://docs.microsoft.com/en-us/analysis-services/multidimensional-models/olap-physical/understanding-microsoft-olap-architecture?view=asallproducts-allversions 3. Adding a KPI to an SQL Server Analysis Services Cube. – [Електронний ресурс], режим доступу: https://www.red-gate.com/simple-talk/databases/sql-server/bi-sql-server/adding-a-kpi-to-an-sql-server-analysis-services-cube/ 4. How to prepare a simple OLAP cube using SQL Server Analysis Services. – [Електронний ресурс], режим доступу: https://www.netwoven.com/2014/06/18/how-to-prepare-a-simple-olap-cube-using-sql-server-analysis-services/
Planned learning activities and teaching methods
Lectures, laboratory work, independent work.
Assessment methods and criteria
Semester assessment: 1. Laboratory work: LO 2.1 – 30/18 points. 2. Laboratory work: LO 2.2 – 30/18 points. Final evaluation (in the form of an exam): - the maximum number of points that can be obtained by a student: 40 points; - learning outcomes that will be assessed: LO1.1, LO1.2; - form of implementation and types of tasks: written work.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Oleksii M. Tkachenko
Theory and Technology of Programming
Faculty of Computer Science and Cybernetics

Departments

The following departments are involved in teaching the above discipline

Theory and Technology of Programming
Faculty of Computer Science and Cybernetics