Diese Seite auf DE
Modul
Theory and Practice of Data Warehousing and Mining [M-INFO-101256]
Credits
9Recurrence
Jedes SemesterDuration
1 SemesterLanguage
Level
4Version
8Responsible
Organisation
- KIT-Fakultät für Informatik
Bricks
Identifier | Name | LP |
---|---|---|
T-INFO-101306 | Datamanagement in the Cloud | 5 |
T-INFO-111400 | Database as a Service | 5 |
T-INFO-112844 | Practical Course: Data Science for Scientific Data | 6 |
T-INFO-113124 | Data Science | 8 |
T-INFO-111622 | Data Science 1 | 5 |
T-INFO-105796 | Practical Course: Analysis of Complex Data Sets | 4 |
T-INFO-103201 | Practical Course: Database Systems | 4 |
T-INFO-108377 | Data Privacy: From Anonymization to Access Control | 3 |
T-INFO-105742 | Big Data Analytics 2 | 3 |
T-INFO-101317 | Deployment of Database Systems | 5 |
T-INFO-106219 | Practical Course: Implementation and Evaluation of Advanced Data Mining Approaches for Semi-Structured Data | 4 |
T-INFO-111626 | Data Science 2 | 3 |
T-INFO-103202 | Analyzing Big Data - Laboratory Course | 6 |
T-INFO-111262 | Practical Course: Data Science | 6 |
T-INFO-101305 | Big Data Analytics | 5 |
Competence Goal
The students
- know the research area of information systems in its various facets and are able to do scientific work in this area,
- are able to explain and to discuss complex aspects of the topics covered by this module with both experts and informed outsiders,
- know the concepts, algorithms, techniques and selected tools in the areas of data warehousing and data mining,
- are familiar with the practical challenges of data analysis and are able to develop respective solutions on their own.
Prerequisites
None
Content
This module aims at exposing students to modern information management, both, in 'breadth´ and 'depth´. We achieve 'breadth´ by means of a close inspection and comparison of different systems and their respective aims. We achieve 'depth´ by means of an extensive examination of the underlying concepts and design alternatives, their assessment as well as by discussing applications. In particular, we look at data warehousing and mining techniques not only from a theoretical point of view but deploy and realise such technologies in a practical course.