1. Introduction to IBM SPSS Modeler
- Introduction to data science
- Describe the CRISP-DM methodology
- Introduction to IBM SPSS Modeler
- Build models and apply them to new data
2. Collect initial data
- Describe field storage
- Describe field measurement level
- Import from various data formats
- Export to various data formats
3. Understand the data
- Audit the data
- Check for invalid values
- Take action for invalid values
- Define blanks
4. Set the unit of analysis
- Remove duplicates
- Aggregate data
- Transform nominal fields into flags
- Restructure data
5. Integrate data
- Append datasets
- Merge datasets
- Sample records
6. Transform fields
- Use the Control Language for Expression Manipulation
- Derive fields
- Reclassify fields
- Bin fields
7. Further field transformations
- Use functions
- Replace field values
- Transform distributions
8. Examine relationships
- Examine the relationship between two categorical fields
- Examine the relationship between a categorical and continuous field
- Examine the relationship between two continuous fields
9. Introduction to modeling
- Describe modeling objectives
- Create supervised models
- Create segmentation models
10. Improve efficiency
- Use database scalability by SQL pushback
- Process outliers and missing values with the Data Audit node
- Use the Set Globals node
- Use parameters
- Use looping and conditional execution