inf541 - Data Challenge (Complete module description)

inf541 - Data Challenge (Complete module description)

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Module label Data Challenge
Modulkürzel inf541
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Verwendbarkeit des Moduls
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule Bereich Wirtschaftsinformatik
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Angewandte Informatik
Zuständige Personen
  • Lehrenden, Die im Modul (module responsibility)
  • Marx Gómez, Jorge (module responsibility)
  • Bremer-Rapp, Barbara (module responsibility)
  • Solsbach, Andreas (module responsibility)
Further responsible persons
Barbara Bremer-Rapp
Prerequisites

useful prior knowledge:

Basics/knowledge of:

  • Python programming and/or R programming
  • Statistics
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
Module contents

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:

  • Exploration and analysis of data
  • Methods of data science (e.g. deep learning)
  • Dealing with programming languages and development frameworks (R, Python, Tensorflow)
  • Hypothesis Formation and Data Storytelling
Literaturempfehlungen
  • Francois Chollet (2017): Deep Learning with Python, Manning.
  • Thomas A. Runkler (2015): Data Mining: Modelle und Algorithmen intelligenter Datenanalyse. Springer Vieweg, Berlin.
  • Wolfgang Ertel (2016): Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung. Springer Vieweg, Berlin.
  • Wes McKinney. (2018): Datenanalyse mit Python: Auswertung von Daten mit Pandas, NumPy und IPython. O'Reilly.
Links

https://uol.de/vlba

Language of instruction German
Duration (semesters) 1 Semester
Module frequency annual
Module capacity 30
Teaching/Learning method Praxisveranstaltung
Examination Prüfungszeiten Type of examination
Final exam of module

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

Portfolio

Form of instruction Practical training
SWS 4
Frequency SoSe oder WiSe
Workload Präsenzzeit 56 h