Worldwide Locations
- Two applications of data mining
- Stages of the CRISP-DM process model
- Successful data-mining projects and the reasons why projects fail
- Skills needed for data mining
2. Working with IBM SPSS Modeler
- 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
- Basic framework of a data-mining project
- Build a model
- Deploy a model
4. Collecting Initial Data
- 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
- How to check for invalid values
- Take action for invalid values
- 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
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