0A004ES ::: Introduction to IBM SPSS Modeler and Data Mining (V15)

Introduction to 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.

Working with Streams

• Describe the different areas of the Modeler User Interface.
• Work with nodes and Supernodes.
• Run, open and save a stream.
• Access the help function within Modeler.

Data Mining Tour

• Explain the primary concepts used in data mining.
• Build, evaluate and deploy a model.
• Use the Sort and Filter nodes.

Collecting Initial Data

• Explain the concepts of "data structure", "records", "fields", "unit of analysis", "storage".
• Read data from and export data to various file formats

Data Understanding

• Examine the distributions of categorical and continuous fields.
• Explain the most common ways of handling missing data.
• Explain how to set Modeler to check data quality and select valid records.

Setting the Unit of Analysis

• Remove duplicate records.
• Aggregate data.

Integrating Data

• Add records from multiple datasets into one dataset.
• Add fields from multiple datasets into one dataset.
• Use sampling for testing purposes.

Deriving and Filling Fields

• Use CLEM to transform data.
• Use the Derive node to create a new field.
• Use the Reclassify node.
• Use the Reorder node to reorder fields.

Looking for Relationships

• Examine the relationship between two categorical fields.
• Examine the relationship between two continuous fields.
• Examine the relationship between one continuous field and one categorical field.

Introduction to Modeling

• Modeling objectives
• Introduction to Classification
• Introduction to Segmentation