The course "Exploratory Data Mining and High-Dimensional Data Modeling with RapidMiner" is a compact two day introduction into the foundations of exploratory data analysis with the Data Mining Software RapidMiner. The methods of exploratory data analysis are often much simpler than statistical modelling schemes. The probably most important aspect of exploratory data mining, however, is the fact that the analyst himself is involved in the analysis process. Hence, exploratory data analysis mainly consists of (semi-)graphical descriptions of data and correlations enabling the analyst to search for patterns or grounding principles. Therefore, exploratory data mining is often the first step in the complete model building process and often delivers models and ideas understandable for non-analysts.

The techniques covered by this course are the visualization of data and simple models together with techniques for getting first insights and hints for further analysis, and the calculation of statistical measures and the analysis of distributions and correlations. Understandable modelling techniques like linear regression or decision trees complement the explorative techniques discussed in this course. Due to a high number of practical exercises, the participants will be able to transfer the gained knowledge to own data mining problems and solve them quickly and easily.

Details

• Course ID: 1003
• Number of days: 2 days
• Location: Dortmund, Germany
• Target audience: users, analysts, developers, administrators
• Previous knowledge: basic knowledge of computer programs and mathematics
• Methods: lectures, discussions, individual and group work, exercises on realistic data. Participants may introduce own work and project specific questions in order to find particular solutions together with the trainer and other participants.
• Content: this course is a compact introduction into the foundations of data mining and into the software RapidMiner. It addresses beginners and intermediate learners. Topics of this course are
  • Introduction into exploratory data analysis and visualization schemes
  • Data Input
  • Visualization techniques: low- and high-dimensional data visualizations, box plots, histograms, Self-Organizing Maps (SOM)
  • Calculation of statistical measurements
  • Calculating correlations, correlations analysis and correlation matrices
  • Visualizing distributions and distinguish between classes
  • Basics of Machine Learning: Naive Bayes, Decision Trees and Linear Regression
  • Validation: introduction into performance criteria, cross validation, bootstrapping
  Extensive exercises on different data sets will be performed for all topics.

Prices

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