0A004 IBM Introduction to IBM SPSS Modeler and Data Mining (V15)
The course structure follows the stages of a typical data mining project, from reading data, to data exploration, data transformation, modeling, and effective interpretation of results.
Who Should Attend
This basic course is for:
- Anyone with little or no experience in using IBM SPSS Modeler
- Anyone with little or no experience in data mining
- Anyone who is considering purchasing IBM SPSS Modeler
1 - 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.
2 - 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
3 - Data Mining Tour
- Explain the primary concepts used in data mining.
- Build, evaluate and deploy a model.
- Use the Sort and Filter nodes.
4 - 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
5 - 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.
6 - Setting the Unit of Analysis
- Remove duplicate records.
- Aggregate data.
7 - Integrating Data
- Add records from multiple datasets into one dataset.
- Add fields from multiple datasets into one dataset.
- Use sampling for testing purposes.
8 - 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.
9 - 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.
10 - Introduction to Modeling
- Modeling objectives
- Introduction to Classification
- Introduction to Segmentation
This is a 2-day class
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