• 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