Course Outline

Sources of methods

  • Artificial intelligence
  • Machine learning
  • Statistics
  • Sources of data

Pre processing of data

  • Data Import/Export
  • Data Exploration and Visualization
  • Dimensionality Reduction
  • Dealing with missing values
  • R Packages

Data mining main tasks

  • Automatic or semi-automatic analysis of large quantities of data
  • Extracting previously unknown interesting patterns
    • groups of data records (cluster analysis)
    • unusual records (anomaly detection)
    • dependencies (association rule mining)

Data mining

  • Anomaly detection (Outlier/change/deviation detection)
  • Association rule learning (Dependency modeling)
  • Clustering
  • Classification
  • Regression
  • Summarization
  • Frequent Pattern Mining
  • Text Mining
  • Decision Trees
  • Regression
  • Neural Networks
  • Sequence Mining
  • Frequent Pattern Mining

Data dredging, data fishing, data snooping

Requirements

Good R knowledge.
  14 Hours
 

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Testimonials

A lot of issues that can be explored after the training

Klaudia Kłębek

Very tailored to needs.

Yashan Wang

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