Detailed Course Outline
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 Modelers user-interface
- • Work with nodes and streams
- • Generate nodes from output
- • Use SuperNodes
- • 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