Every summer semester
5 Credit points
For information on the duration of the modul, refer to the courses of study in which the module is used.
Non-official translation of the module descriptions. Only the German version is legally binding.
Students learn in the lectures and are the exercise courses how to integrate heterogeneous data sources, i.e. how to combine data residing in different data sources to obtain a global view of the data relating to relevant entities, which represents one of the major challenges in data management especially in the Big Data era as this integration is key to addressing the issue of variety. The problem has been considered for decades, and the lectures will cover foundations of data integration as well as algorithmic and system aspects. The module includes an exam at the end of the term.
Topics covered in this module:
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Module structure: 1 bPr 1
Portfolio consisting of per default weekly exercises or programming tasks and final written exam (per default 60 minutes) or final oral exam (per default 15 minutes). The exercises are based on the content of the lecture and enable students to train and further investigate the topics. It is required that a sufficient percentage of the exercises are successfully completed (per default 50% of the total number of points which can be achieved during a semester). The final oral exam concerns both, the content of the lecture as well as the exercises.
| Degree programme | Profile | Recommended start 3 | Duration | Mandatory option 4 |
|---|---|---|---|---|
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 1 | 2. | one semester | Compulsory optional subject |
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 2 | 2. | one semester | Compulsory optional subject |
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