Data Mining and Business Analytics with RapidMiner and RapidAnalytics 2

This training course is a compact two day introduction into additional possibilities of performing data mining and business analytics with the software RapidMiner and the data analysis server RapidAnalytics. After this training course the participants will have a complete understanding of how Rapid-I software works and is used. The participants will be able to prepare data and create predictive models in standard data environments typically found within most analyst positions as well as in many more uncommon environments. 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 the most necessary and common data preparations,
• build sophisticated predictive models,
• evaluate the model’s quality with respect to different criteria,
• deploy data mining models.

Details

• Course ID: 102
• 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, but we recommend to visit the course “Data Mining / Predictive Analytics with RapidMiner and RapidAnalytics 1” before visiting this one.

Topics: The topics include:

• Data Preparation
• Changing value types by discretization and dichotimization
• Balancing data
• Joins
• Detection and removal of outliers
• Dimensionality reduction
• Predictive Models
• Neural Networks
• Logistic Regression
• Meta Learning: Bagging and Boosting
• Model Evaluation
• Advanced performance criteria
• Comparison between models
• Significance tests
• Validation of preprocessing and preprocessing models
• Scoring
• Scaling confidences
• Filter uncertain predictions
• Deployment
• Sharing data, models, and processes
• Exporting processes as web service
• Basics of report creation
• Managing processes and services
• Remote execution of processes
• Scheduling processes

Prices

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

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