Introduction to IBM SPSS Modeler and Data Mining (v18) SPVC

1: Introduction to data mining
• List two applications of data mining
• Explain the stages of the CRISP-DM process model
• Describe successful data-mining projects and the reasons why projects fail
• Describe the skills needed for data mining

2: Working with IBM SPSS Modeler
• Describe the MODELER user-interface
• Work with nodes
• Run a stream or a part of a stream
• Open and save a stream
• Use the online Help

3: Creating a data-mining project
• Explain the basic framework of a data-mining project
• Build a model
• Deploy a model

4: Collecting initial data
• Explain the concepts "data structure", "unit 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
• Explain how to check for invalid values
• Take action for invalid values
• Explain how to define blanks

6: Setting the unit of analysis
• Set the unit of analysis by removing duplicate records
• Set the unit of analysis by aggregating records
• Set the unit of analysis by expanding a categorical field into a series of flag fields

7: Integrating data
• Integrate data by appending records from multiple datasets
• Integrate data by merging 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 modeling objectives
• Use a classification model
• Use a segmentation model