Data Mining and Business Analytics with RapidMiner and RapidAnalytics 1

This training course is a compact two day introduction into the foundations of data mining and business analytics as well as into the software RapidMiner and RapidAnalytics. After this training course the participants will have a complete understanding of how Rapid-I software works and is used. The overall data mining process is covered as well as the foundations of the software. Participants will be able to create predictive models in standard data environments typically found within most analyst positions. 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.

After the training course the participants will have the ability to

• perform basic data preparations,
• build first predictive models,
• evaluate the model’s quality,
• score new data sets.

Details

• Course ID: 101
• 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. The training course addresses beginners and intermediate learners.

Topics: The topics include:

• Basic Usage
• User Interface
• Creating and handling RapidMiner repositories
• Starting a new RapidMiner project
• Operators and processes
• Loading data from flat files
• Loading data from databases
• Storing data, processes and results
• Data Preparation
• Normalization and standardization
• Basic transformations of value types
• Handling missing values
• Sampling
• Filtering examples and attributes
• Handling attribute roles
• Predictive Models
• Explorative data analysis including data visualization
• Correlations
• Linear Regression
• Naïve Bayes
• Decision Trees
• Rules
• Importance of attributes
• Model Evaluation
• Splitting data
• Evaluation methods
• Performance criteria
• Lift charts
• ROC plots
• Scoring
• Applying models

Prices

Value added tax (VAT) may have to be added to these prices.

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