inf541 - Data Challenge (Course overview)

inf541 - Data Challenge (Course overview)

Department of Computing Science 6 KP
Semester courses Sommersemester 2023
Lehrveranstaltungsform: Practical training
  • No access 2.01.541 - Data Challenge Show lecturers
    • Marius Wybrands
    • Dr.-Ing. Andreas Solsbach
    • Barbara Bremer-Rapp

    Dates on Friday, 14.04.2023 09:00 - 10:00, Friday, 08.09.2023 08:30 - 13:00
Hinweise zum Modul
Prerequisites

useful previous knowledge: Business Intelligence I, Business Intelligence II

Kapazität/Teilnehmerzahl 30
Prüfungszeiten

During the semester break, after the end of the lecture period

Module examination

Portfolio

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.
Professional competence
The students:

  • learn how to handle structured and unstructured data sources.
  • acquire practical knowledge about different methods of data science.
  • learn basic procedures in the implementation of data science projects.
  • follow and refine the implementation of the practical learning by means of a partly given model scenario, but also by self-initiatives.


Methodological competence
The students:

  • are able to explore and analyze data sets
  • recognize connections in large data sets
  • form a hypothesis for the solution of a business problem.


Social competence
The students:

  • work in groups, identify work packages and take on responsibility for the jobs assigned to them.
  • discuss and introduce the results on a functional level.


Self-competence
The students:

  • reflect their approach on the basis of self-defined goals.
  • collect and analyze required information.
  • prepare the collected information in a target group-oriented manner