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Datamining

Datamining

Goal

Applicable to different fields as diverse as the customer intelligence, financial scoring or predictive analysis on customer retention and loss fields, not a modern organization that does not use data mining to obtain clear competitive advantages is conceived.

In our implementations, we use advanced techniques using a variety of algorithms, manually creating decision trees, complex logic, etc.

Mining opens a field of great potential for treating large volumes of data, by performing complex calculations and simulations.

Target audiences

Professionals information technology, IT managers, business analysts, systems analysts, Java architects, system developers, database administrators, developers and professionals in relation to the area of technology, marketing, business and financial.

Observations

Certification

All students receiving the course will be given a certificate of attendance.

Syllabus

What Data Mining is. Introduction and examples
Data Mining Algorithms
  • Clustering algorithms : KNN
  • Decision Trees
  • Linear regression
  • Bayesian algorithms
Data Mining Tools : Weka
  • Data Format
  • Data Exploration
  • Classification algorithms
  • Display
  • KnowledgeFlow
  • Experimenter
Simple examples on different algorithms and analysis of the results obtained

Contacto

Ajustamos cada curso a sus necesidades.

Nuestra oficina en Madrid

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