General Information

Lecture

Data Mining
Type of database analysis that attempts to discover useful patterns or relationships in a group of data. The analysis uses advanced statistical methods, such as cluster analysis, and sometimes employs artificial intelligence or neural network techniques. A major goal of data mining is to discover previously unknown relationships among the data, especially when the data come from different databases. Businesses can use these new relationships to develop new advertising campaigns or make predictions about how well a product will sell. […]

Encyclopaedia Britannica

Module Assessment

  • The assessment for this lecture consists of working on a case study.
  • The case study is completed as group work in teams.
  • The work period spans the entire semester.
  • For grading, you must submit a case study report as Jupyter Notebook including the Python code used for the analyses.
  • The results must be submitted at the end of the semester via Teams.

Tutorial

For working on the case study, you may make use of support from our tutor. Communication generally takes place via Teams. Details will be announced over the course of the lecture.

Software

  • In this lecture, we use the programming language Python and the development environment Jupyter Hub.
  • It is strongly recommended to use this environment, but you may also install Python and Jupyter yourself on your own infrastructure.

Self-Learning Platform: DataCamp

You have the opportunity to expand your methodological knowledge and your practical skills in Python (or other analytical tools) with the self-learning platform DataCamp. In order to get free access to all DataCamp courses, register with your FH email address here.

ILIAS

ILIAS won’t be actively used in this lecture

MS Teams

  • For collaboration during the lecture and the case study we use Microsoft Teams.
    • Questions in Teams are answered by the tutors and by me.
    • Please use the public channels to ask questions so other teams can benefit from the answers as well.
    • For larger issues, personal appointments are also possible.
  • In the sessions, we will work together on the necessary toolkit (e.g., fundamentals and methods of data mining, implementation using the Python programming language).
  • For each topic block, we will carry out practical exercises using an example datasets.
  • In the sessions, we will also discuss frequently asked questions together.
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