0A003G: Introduction to IBM SPSS Modeler and Data Mining (V14.2)

$1,595.00


  • classroom

  • virtual

  • Onsite

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Duration: 2 Days

Introduction to IBM SPSS Modeler and Data Mining is a two day classroom basic 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 reading data, to data exploration, data transformation, modeling, and effective interpretation of results. The course provides training in the basics of how to read, explore, and manipulate data with IBM SPSS Modeler, and then create and use successful models.

What You Will Learn

 

Audience

 

  • 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

Prerequistes

 

  • General computer literacy

No statistical background is necessary.

It would be helpful if you had an understanding of your organization's data, as well as any of your organization's business issues that are relevant to the use of data mining.

Course Outline

 

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.

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.

Data Mining Tour

  • Explain the primary concepts used in data mining.
  • Build, evaluate and deploy a model.
  • Use the Sort and Filter nodes.

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

Data Understanding

  • Examine the distributions of categorical and continuous fields.
  • Explain the most common ways of handling missing data.
  • Explain the most common ways of handling outliers.
  • Explain how to set Modeler to check data quality and select valid records.

Setting the Unit of Analysis

  • Remove duplicate records.
  • Aggregate data.
  • Create flag fields.
  • Restructure continuous fields.

Integrating Data

  • Add records from multiple datasets into one dataset.
  • Add fields from multiple datasets into one dataset.
  • Use sampling for testing purposes.

Deriving and Filling Fields

  • Use CLEM to transform data.
  • Use the Derive node to create a new field, and the Filler node to replace values in a field.
  • Explain how to automatically generate a Derive node.
  • Use the Reorder node to reorder fields.

Reclassifying and Binning Fields

  • Use the Reclassify node.
  • Explain how to automatically generate a Reclassify node.
  • Use the Binning node.

Looking for Relationships

  • Examine the relationship between two categorical fields.

Introduction to Classification

  • Differentiate between predictive modeling and other types of modeling.
  • Differentiate between various types of predictive models.
  • Run CHAID in interactive mode.
  • Run CHAID and various other models in automatic mode.

Course Labs