Introduction to IBM SPSS Modeler and Data Science (v18.1.1)

Audience

• Business analysts
• Data scientists
• Clients who are new to IBM SPSS Modeler or want to find out more about using it

Prerequisites

• It is recommended that you have an understanding of your business data

Course Topics

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