Infopaket

Toolkit for Developing Information-Analytical Systems

ДВС.3.02.05

Вибіркова дисципліна для ОП

Second

2022/2023

3 Semester

4

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.

Distance form

Know: the basics of manipulating tabular data, the concept of relational databases and their transformations, in particular, normalization and denormalization. Be able to: apply programming and data processing tool environments in practice. Possess skills in: visual and verbal programming.

The purpose of the discipline is to acquire basic knowledge and skills in using tool environments to develop information and analytical systems, and to conduct business analysis to support decision-making. The academic discipline “Information and Analytical Systems Development Toolkit” is a component of the educational and scientific program for training specialists at the second (master's) level of higher education in the field of knowledge 12 “Information Technologies” in the specialty 122 “Computer Science” of the educational and scientific program “Business Informatics”. It is taught in the 1st semester of the 2nd year in the amount of 120 hours (4 ECTS credits), including: lectures – 34 hours, consultations – 6 hours, independent work – 80 hours. The discipline ends with a test.

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.

Lectures, independent work.

Semester assessment: test 1 – 30/18 points; test 2 – 30/18 points. Final assessment (in the form of a credit): form of conduct and types of tasks: calculation of points for the semester.

Ukrainian