Schulung - IBM 0A008G - Introduction to IBM SPSS Modeler and Data Science (v18.1.1)

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 Modeler's 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

Objective

• Introduction to data science
• Introduction to IBM SPSS Modeler
• Introduction to data science using IBM SPSS Modeler
• Collecting initial data
• Understanding the data
• Setting the of analysis
• Integrating data
• Deriving and reclassifying fields
• Identifying relationships
• Introduction to modeling