SylabUZ
Nazwa przedmiotu | Data warehouses and reporting services |
Kod przedmiotu | 11.3-WE-BizElP-DWandRS-Er |
Wydział | Wydział Informatyki, Elektrotechniki i Automatyki |
Kierunek | Biznes elektroniczny |
Profil | praktyczny |
Rodzaj studiów | Program Erasmus pierwszego stopnia |
Semestr rozpoczęcia | semestr zimowy 2019/2020 |
Semestr | 3 |
Liczba punktów ECTS do zdobycia | 5 |
Typ przedmiotu | obowiązkowy |
Język nauczania | angielski |
Sylabus opracował |
|
Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
Wykład | 15 | 1 | - | - | Zaliczenie na ocenę |
Laboratorium | 30 | 2 | - | - | Zaliczenie na ocenę |
Projekt | 15 | 1 | - | - | Zaliczenie na ocenę |
Familiarize students with the architectures of the data warehouses and the data life cycle in the data warehouse. Presentation of the software used to design the OLAP data structures. Developing the skills of designing and implementing data warehouses. Presentation of data reporting methods. Developing the ability to create reports using charts and pivot tables. Presentation of examples of data warehouse applications in e-business.
Databases
Data warehouse architecture. Characteristics of data warehouse subsystems. Review and characteristics of popular data warehouse systems present in IT market.
Data warehouse design. Conceptual, logical and physical model. Types of data warehouses. Data flow from source to target systems. Presentation of tools supporting data warehouse design.
OLAP cubes. Multidimensional data structures. The concept of fact table, measure, dimension, and attribute. Star and snowflake schema. Characteristics of typical operations on multidimensional data cubes. Practical exercises from the design and implementation of OLAP cubes.
Reporting based on multidimensional data cubes. Methods of generating queries for data cubes. Pivot tables. Methods of graphic representation of data. A query language for multidimensional data. Practical exercises involving the preparation of a given report based on data from a multidimensional data cube.
Discussion of examples of data warehouse applications in e-business. Presentation of sample data warehouse projects.
Lecture - conventional lecture using a video projector.
Laboratory - practical exercises in the computer laboratory.
Project - project implementation in a computer laboratory.
Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
Lecture - the passing criteria is to obtain positive grades from tests carried out at least once in a semester.
Laboratory - the passing criterion is to obtain positive marks for laboratory exercises and tests.
Project - positive assessment of the project or projects realized during the semester
Final mark components = lecture: 30% + teaching laboratory: 40% + project: 30%
Zmodyfikowane przez dr hab. inż. Marek Kowal, prof. UZ (ostatnia modyfikacja: 09-12-2019 15:17)