About this Course

Introduction to IBM SPSS Modeler and Data Mining (V16) is a two day course, that provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.

Audience Profile

This basic course is for:

• Anyone who is new to IBM SPSS Modeler
• Anyone considering purchasing IBM SPSS Modeler
• Anyone interested in Data Mining

Prerequisites

You should have:

• General computer literacy

Course Outline

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 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. A Data-Mining Tour

• 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 your 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. Looking for 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