Tools for the development of information-analytical systems
Course: Business Informatics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Tools for the development of information-analytical systems
Code
ВК.4.03.01
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
3 Semester
Number of ECTS credits allocated
4
Learning outcomes
PLO7. Define methodological principles and methods of scientific research in the field of information technology depending on the object and subject, using an interdisciplinary approach.
PLO9. Evaluate, classify, justify and formulate requirements for information and analytical systems that are created and implemented using various methods and technologies.
PLO10. Analyze, evaluate and select modern tools and computing tools, technologies, algorithmic and software solutions for a specific task in the field of computer science and information technology.
PLO12. Understand, purposefully search, analyze and choose in information and reference and scientific and technical resources and sources necessary to solve professional and scientific problems of modern achievements of science and technology in view of the values of modern society.
Form of study
Distance form
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. Be able to: apply in practice tool environments for programming and data processing.
3. Have the skills of visual and language programming.
Course content
The purpose of the discipline is to master basic knowledge and master the skills of using tool environments for the development of information and analytical systems, conducting business analysis to support decision making.
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 dashboards, create and export pivot analytical tables for various purposes: from expanded to maximally compressed (KPI level) with trend reflection.
Recommended or required reading and other learning resources/tools
1. Understanding Microsoft OLAP Architecture https://docs.microsoft.com/en-us/analysis-services/multidimensional-models/olap-physical/understanding-microsoft-olap-architecture
2. Soheil B. Expert Data Modeling with Power BI: Get the best out of Power BI by building optimized data models for reporting and business needs, 2020, 575p.
3. Roldan M.C. Learning Pentaho Data Integration 8 CE - Third Edition: An end-to-end guide to exploring, transforming, and integrating your data across multiple sources 2017, 501p.
4. Leskovec J. Mining of Massive Datasets / Jure Leskovec Anand Rajaraman, Jeffrey David Ullman // Stanford Univ. – 2010.
5. Paklin N.B. Biznes-analitika: ot dannyih k znaniyam / Paklin N.B., Oreshkov V.I. // Piter, 2013. – 706 c.
Planned learning activities and teaching methods
Lectures, independent work, control works, test.
Assessment methods and criteria
- Semester assessment:
1. Control work: LO 1.1., LO 1.2 — 30/18 points.
2. Control work: LO 2.1., LO 2.2 — 30/18 points.
- Final assessment (in the form of a test):
maximum number of points that can be obtained by a student: 40 points;
learning outcomes that will be evaluated: LO 1.1, LO 1.2, LO 2.1, LO 2.2.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Taras
V.
Panchenko
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