SylabUZ
| Course name | Data warehouses and reporting services |
| Course ID | 11.3-WE-BizElP-DWandRS-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | E-business |
| Education profile | practical |
| Level of studies | First-cycle Erasmus programme |
| Beginning semester | winter term 2019/2020 |
| Semester | 3 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 15 | 1 | - | - | Credit with grade |
| Laboratory | 30 | 2 | - | - | Credit with grade |
| Project | 15 | 1 | - | - | Credit with grade |
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.
| Outcome description | Outcome symbols | Methods of verification | The class form |
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%
Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 09-12-2019 15:17)
