Rapid miner

//Rapid miner
Rapid miner2019-09-02T17:07:06+01:00

Training Information

The increasing digitalisation generates a tremendous amount of data in every company.

For data analysis purposes there have been applied statistical software packages in the past but Big Data analytics require Data Mining methods.

Different software packages are available, for example JMP-PRO, Knime or Rapid Miner. You can perform a download of this software very easily.

Length / Duration:2 days (from 9:00-17:00 Uhr)
Cost excl. VAT
QualificationIntroduction into Rapid Miner
Minimum participants3
PrerequisitesBasic statistical knowledge as well as IT-and programming experience might help but not a “must”
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  • You will get an overview about the Data Mining toolbox and about the opportunities of application
  • You will learn different typical real scenarios which require Data Mining
  • You will learn the first steps to get started with Rapid Miner
  • You will be able to import data, to create processes and graphics and how to store
  • You will get an overview about Predictive Modelling
  • You will learn how to develop a systematic approach into Data Mining (Roadmap)

Target group:

Employees and Managers from IT, R&D, Engineering, Production and QM but also Sales and Marketing, industrial and service companies

Training contents:

  • User Interface
  • Operators and processes
  • Development and Management of Rapid Miner repositories
  • Import of data, handling of different sources
  • Data preparation
  • Definition of attributes based on a real situation
  • CRISP-Model
  • Storage of data and processes and other results like graphs
  • Visualisation and basic charting
  • Predictive Modelling, focus on decision trees, neural nets, linear and logistic regression
  • Presentation and discussion of case studies

Scope of services:

  • Detailed training documentation (in paper and as .pdf.-files)
  • Coaching of your project ba an experienced Master Black Belt (limited)
  • Interactive workshops
  • Certificate ” Rapid Miner basics ”
  • Free space for exchange and knowledge transfer