Attention: This page shows a discontinued module offer.
To be discontinued
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 competencies in performing data mining tasks on very large amounts of data that cannot be stored in main memory. The lectures provide the key ideas of similarity search using minhashing and locality-sensitive hashing, of data stream processing where data arrives so fast that it has to be processed immediately or is otherwise lost, of Web-related algorithms such as Google's PageRank, of algorithms for mining frequent itemsets, association rules and frequent subgraphs, of algorithms to analyze the structure of large graphs such as social network graphs, and of the map-reduce principle to design parallel algorithms. The module includes an exam at the end of the term.
The module Big Data Analytics deals with methods and algorithms in the context of big data analytics. In particular, the following topics are addressed:
Knowledge about databases can be helpful.
—
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.
Recommended literature:
Previously, the programme was offered every summer semester.
| 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 | Obligation |
| 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 |
The system can perform an automatic check for completeness for this module.