Information processing and analysis technologies
Course: Applied mathematics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Information processing and analysis technologies
Code
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
4
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. Ability to design and use existing data integration tools, process data stored in different systems.
PLO7. Ability to organize, configure and develop Web-systems using the principles of distributed systems, hypertext systems, relevant hardware and software.
PLO10. Ability to build models of physical and production processes, design storage and data space, knowledge base, using charting techniques and standards for information systems development.
Form of study
Prerequisites and co-requisites
1. Know: the basics of tabular data manipulation, the concept of relational databases and their transformations, in particular, normalization and denormalization.
2. To be able: to apply in practice tool environments of programming and data processing.
3. Have the skills: visual design.
Course content
The purpose of the discipline - the acquisition of basic knowledge and skills of data processing technologies and development of information and analytical systems.
As a result of studying the discipline the student must:
know the basic concepts of OLAP-technology for working with databases, development of data warehouses, OLAP-modeling cubes;
be able to apply in practice tool environments in the design and development of analytical integrated interactive reports and panels (dashboards), create and export pivot analytical tables.
Recommended or required reading and other learning resources/tools
1. Leskovec J. Mining of Massive Datasets / Jure Leskovec Anand Rajaraman, Jeffrey David Ullman // Stanford Univ. – 2010.
2. Barsegyan A.A. Metodyi i modeli analiza dannyih: OLAP i Data Mining // BHV-Peterburg, 2004. – 331с.
3. Paklin N.B. Biznes-analitika: ot dannyih k znaniyam / Paklin N.B., Oreshkov V.I. // Piter, 2013. – 706 c.
4. Understanding Microsoft OLAP Architecture https://docs.microsoft.com/en-us/analysis-services/multidimensional-models/olap-physical/understanding-microsoft-olap-architecture.
Planned learning activities and teaching methods
Lectures, laboratory classes, independent work, tests, defense of laboratory work, exam.
Assessment methods and criteria
- Semester assessment:
1. Protection of laboratory works: LO 2.1 –– 30/18 points.
2. Protection of laboratory works: LO 2.2 –– –– 30/18 points.
Final assessment (in the form of an exam):
- maximum number of points: 40 points;
- learning outcomes which shall be assessed: LO1.1, LO1.2.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Volodymyr
F.
Kuzenko
Theory and Technology of Programming
Faculty of Computer Science and Cybernetics
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