Development of business analytical systems

Course: Informatics

Structural unit: Faculty of Computer Science and Cybernetics

Title
Development of business analytical systems
Code
ДВС.2.08
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
8 Semester
Number of ECTS credits allocated
4
Learning outcomes
PLO 20.2. It is motivated to choose programming technologies to solve the tasks of creating and maintaining software. PLO 21.2. Be able to research and document the existing business processes of the customer's organization.
Form of study
Prerequisites and co-requisites
1. To know: basic concepts of the basics of databases, principles of system design. 2. Be able to: analyze system design requirements and formalize them. 3. Have skills: in mathematical logic and programming.
Course content
Part 1. Development and deployment of multidimensional data warehouses and reporting systems Topic 1. Concepts of OLAP and BI. Multidimensional cubes, ETL. Topic 2. Definition and deployment of the cube: dimensions, axes, attributes, hierarchies. Topic 3. Management of attributes and axes. Relationships between axes and dimensions. Topic 4. Calculated members of dimensions. KPIs. Topic 5. Actions. Drillthrough. Perspectives of the cube. Roles. Topic 6. Report development environment. Topic 7. Creation of basic reports. Topic 8. Grouping, sorting, formatting in the report. Topic 9. Report parameters. Topic 10. Advanced parameters. Topic 11. Ad hoc reporting. PivotTable Summary Tables (OWC). Topic 12. Security. Administration of the report server. Part 2. Data mining and query languages ​​in multidimensional environments Topic 13. Creation of models and structures for data mining (Data Mining). Topic 14. Algorithms for building models. Topic 15. Model research and testing. Topic 16. Creating forecasts and working with them. Microsoft Time Series. Topic 17. Algorithms for determining relationships and analyzing sequences. Topic 18. Neural network and logistic regression algorithms. Topic 19. Query language for MDX OLAP cubes: syntax and semantics. Topic 20. MDX Queries. Computing context and additional capabilities. ..
Recommended or required reading and other learning resources/tools
Basic: 1. www.msdn.com 2. Braian Larson. Razrabotka biznes-analitiki v Microsoft SQL Server 2005 : [effektivnoe priniatie reshenii, vitriny dannykh, sluzhby integratsii, intellektual-nyi analiz dannykh : per. s angl.] / B. Larson. — SPb. [i dr.] : Piter, 2008. — 683 s. : il., tabl. — (Biblioteka programmista). Additional: 3. Tom DeMarco. The Deadline: A Novel about Project Management / Tom DeMarco., 1997. – 130 p. 4. Alexander Osterwalder & Yves Pigneur. Business Model Generation/ Alexander Osterwalder, Yves Pigneur. Business Model Generation, 2013. 5. www.google.com 6. http://technet.microsoft.com/en-us/library/ms170208.aspx 7. http://technet.microsoft.com/en-us/library/ms167167.aspx 8. http://technet.microsoft.com/en-us/library/cc879271.aspx 9. http://msdn.microsoft.com/en-us/library/ms167167(SQL.90).aspx
Planned learning activities and teaching methods
Lectures, consultations, independent work
Assessment methods and criteria
- semester assessment: 1. Control work 1: RN 1.1., RN 1.2, — 24 points/14.4 points. 2. Control work 2: РН1.3, РН2.1, РН3.1, РН4.1 - 24 points/14.4 points. 3. Current assessment: PH2.1, PH3.1, PH4.1 - 12 points/7.2 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 evaluated: PH1.1, PH1.2, PH1.3; - form of implementation and types of tasks: written
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline