Course Outline
Introduction
- Overview of advanced analytics and data mining
- Overview of CRISP-DM
- Understanding the Modeler UI
- Understanding the mechanics of building streams
Understanding Data
- Reading data into Modeler
- Measurement level and field roles
- Using the data audit node
Data Preparation
- Selecting cases
- Reclassifying categorical values
- Using append node and merge node
- Deriving fields
Modeling
- Overview of modeling
- Using a partition node
- Building a CHAID model
- Model assessment
Evaluation and Deployment
- Using analysis and evaluation node
- Scoring new data and exporting
- Using flat file node
Troubleshooting
Summary and Next Steps
Requirements
- No data mining background needed
Audience
- Data analysts
- Anyone who wants to learn about SPSS Modeler
Testimonials (5)
how the trainor shows his knowledge in the subject he's teachign
john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course - Data Vault: Building a Scalable Data Warehouse
Prepared material. Full professionalism. Very good contact with the trainer. Full engagement and openness to changing the planned training format (very valuable open discussions on the topics we prepared)
Kamil Trebacz - Bank Gospodarstwa Krajowego
Course - Pentaho Data Integration (PDI) - moduł do przetwarzania danych ETL (poziom zaawansowany)
Machine Translated
Open discussion with trainer
Tomek Danowski - GE Medical Systems Polska Sp. Z O.O.
Course - Process Mining
I genuinely enjoyed the hands passed exercises.
Yunfa Zhu - Environmental and Climate Change Canada
Course - Foundation R
The example and training material were sufficient and made it easy to understand what you are doing.