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[ 921CGELCDAP21 ] PR Computational Data Analytics
Versionsauswahl
Es ist eine neuere Version 2025W dieser LV im Curriculum Master's programme Polymer Engineering and Science (PES) 2025W vorhanden.
Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS M1 - Master's programme 1. year Computer Science Johannes Fürnkranz 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2022W
Objectives This practical course accompanies the lecture Computation Data Analytics with practical exercises with data mining software. Students are able to gain practical experience with the tools that are discussed in the lecture.
Subject Topics covered include

  • Data mining process models
  • Pre-processing techniques
  • Inductive rule learning
  • Efficient similarity-based techniques
  • Association rule mining
  • Stream Mining
  • Evaluation

using common data mining software, such as Weka, KNIME, RapidMiner, Orange, or similar.

Criteria for evaluation Hands-on Exercises
Methods Hands-on Experience on data mining software
Language Englisch
Study material
Changing subject? No
On-site course
Maximum number of participants 35
Assignment procedure Direct assignment