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Introduction to IBM^® SPSS^® Modeler* and Data Mining

Course Description

This course provides you with 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.  You’ll follow the stages of a typical data mining project, from reading data, to data exploration, data transformation, modelling and effective interpretation of results.  You’ll also learn how to read, explore and manipulate data with IBM SPSS Modeler and then create and use successful models.

Who Should Attend

• Anyone with little or no experience using IBM SPSS Modeler.  May also be unfamiliar with data mining in general

Prerequisites

• General computer literacy
• It would be helpful if you had an understanding of your organisation’s data, as well as any of your organisation’s business issues that are relevant to the use of data mining
• No statistical background is necessary

Course Content

Following an overview of the main features and an introduction to essential terminology, you will proceed logically through the following topics:

• Introduction to data mining
• The CRISP-DM methodology
• Best practices for data mining
• The basics of using IBM SPSS Modeler
• Reading data files
• Working with dates
• Auditing and exploring data quality
• Searching for anomalous data and outliers
• Data manipulation
• Searching for relationships among fields
• Combining data files by appending and/or merging
• Restructuring data files with aggregate
• Sampling data
• Partitioning data for modelling
• Modelling techniques in IBM SPSS Modeler
• Automatic modelling for binary outcomes
• Evaluating and comparing model performance
• Deploying and using models
• Running IBM SPSS Statistics* commands from IBM SPSS Modeler

SPSS Products Used

• IBM SPSS Modeler

Duration

• 3 days

Level: Beginner

*IBM SPSS Modeler formerly known as PASW Modeler/Clementine.
IBM SPSS Statistics formerly known as PASW Statistics.

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