1. Introduction to data science
- List two applications of data science
- Explain the stages in the CRISP-DM methodology
- Describe the skills needed for data science
2. Introduction to IBM SPSS Modeler
- Describe IBM SPSS Modeler's user-interface
- Work with nodes and streams
- Generate nodes from output
- Use Super Nodes
- Execute streams
- Open and save streams
- Use Help
3. Introduction to data science using IBM SPSS Modeler
- Explain the basic framework of a data-science project
- Build a model
- Deploy a model
4. Collecting initial data
- Explain the concepts "data structure", "of analysis", "field storage" and "field measurement level"
- Import Microsoft Excel files
- Import IBM SPSS Statistics files
- Import text files
- Import from databases
- Export data to various formats
5. Understanding the data
- Audit the data
- Check for invalid values
- Take action for invalid values
- Define blanks
6. Setting the of analysis
- Remove duplicate records
- Aggregate records
- Expand a categorical field into a series of flag fields
- Transpose data
7. Integrating data
- Append records from multiple datasets
- Merge fields from multiple datasets
- Sample records
8. Deriving and reclassifying fields
- Use the Control Language for Expression Manipulation (CLEM)
- Derive new fields
- Reclassify field values
9. Identifying relationships
- Examine the relationship between two categorical fields
- Examine the relationship between a categorical field and a continuous field
- Examine the relationship between two continuous fields
10. Introduction to modeling
- List three types of models
- Use a supervised model
- Use a segmentation model