SylabUZ
| Course name | Data Warehouse |
| Course ID | 11.3-WK-MATD-HD-L-S14_pNadGenWVOWZ |
| Faculty | Faculty of Mathematics, Computer Science and Econometrics |
| Field of study | Mathematics |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MS degree |
| Beginning semester | winter term 2019/2020 |
| Semester | 2 |
| ECTS credits to win | 5 |
| Course type | optional |
| Teaching language | polish |
| Author of syllabus |
|
| 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 |
| Laboratory | 30 | 2 | - | - | Credit with grade |
| Lecture | 15 | 1 | - | - | Credit with grade |
The aim of the course is present the theory in designing a data warehouse, knowledge tools for building queries and reports, and business intelligence.
Information Technology, Database.
Lecture:
1. Evolution of Decision Support Systems (DSS).
2. Introduction to Data Warehousing (definitions and terminology).
3. Data Warehouse Architecture (conceptual model, logical and physical).
4. Data Warehouse Design (models multidimensional OLAP operations)
5. Data Modeling for Data Warehouse (modeling point).
6. Physical implementation of data warehouse (extraction and loading).
7. Data Warehouse Systems (overview of the typical solutions.
Laboratory:
1. Introduction to DB2 Web Query.
2. Create and edit synonyms.
3. Create a simple report (Report Assistant).
4. Creating graphs (Graph Assistant).
5. Metadata Tools (Converting Existing Query Reports)
6. Create and use active reports (Active Reports).
7. Using OLAP (Online Analytical Processing).
Lecture: the traditional lecture.
Laboratory: individual work at the computer. Processed material according to instructions that every
student gets at the beginning of class. Discussions leading to deepen knowledge and understanding of
the processed material.
| Outcome description | Outcome symbols | Methods of verification | The class form |
1. Checking the degree of student preparation and their activities during the classes.
2. Getting good ratings from all the laboratory to be implemented under the laboratory.
3. Written colloquium at the end of the course.
1. Chris Todman, Designing A Data Warehouse: Supporting Customer Relationship Management,
Prentice Hall, 2001.
2. Ramez Elmasri, Shamkant B. Navathe. Wprowadzenie do systemów baz danych, Helion 2005.
1. William Harvey Inmon, Building the Data Warehouse. 4th Edition, Wiley 2005.
2. Ralph Kimball, Margy Ross, The Data Warehouse Toolkit: The Complete Guide to Dimensional
Modeling. 2nd Edition, Wiley 2002.
3. Adam Pelikant, Hurtownie danych. Od przetwarzania analitycznego do raportowania, Helion 2011.
Modified by dr Alina Szelecka (last modification: 03-07-2019 12:29)
