|Module label||Data Challenge|
|Credit points||6.0 KP|
|Institute directory||Department of Computing Science|
|Applicability of the module||
useful previous knowledge: Business Intelligence I, Business Intelligence II
|Skills to be acquired in this module||
After successful completion of the course, students should be able to answer specific, entrepreneurial questions with the help of data-driven methods. The handling of data should be mastered unerringly in the programming languages Python and/or R. Furthermore, competences in the field of algorithmics and data storytelling should be developed.
The module teaches basic skills in the field of data science and the application of various methods and algorithms. The cooperation with a practice partner ensures that the students work on a problem that is as real and practical as possible. By working independently on the problem and the final presentation of the results, further soft skills of the students will be trained.
If methodological competence in the field of data science is to be learned and expanded, this is usually only possible with the help of open available, idealized data sets and exemplary tasks. Basic programming skills can be acquired in this way, but dealing with real business problems and solving them with the help of data science methods can only be learned through practice. In this module, a real problem of a practice
partner is presented, this partner provides data and domain knowledge and then a data-centered solution for this problem must be designed and implemented independently.
Within the module, the following topics are dealt with:
|Language of instruction||German|
|Duration (semesters)||1 Semester|
|Type of module|
|Teaching/Learning method||PR Blockseminar|
|Previous knowledge||nützliche Vorkenntnisse: Business Intelligence I, Business Intelligence II|
|Examination||Examination times||Type of examination|
|Final exam of module||
During the semester break, after the end of the lecture period
|Type of course||Practical training
|Frequency||SoSe oder WiSe|
|Workload Präsenzzeit||56 h|