IBM SPSS Modeler Foundations V18.2 0A069G

Duration: 
2 days
Codes: 
0A069G
Versions: 
V18.2

Overview

This course provides the foundations of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The course provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and introduces the student to modeling.

Audience

  • Data scientists
  • Business analysts
  • Clients who are new to IBM SPSS Modeler or want to find out more about using it

Skills Gained

  • Introduction to IBM SPSS Modeler
  • Collect initial data
  • Understand the data
  • Set the unit of analysis
  • Integrate data
  • Transform fields
  • Further field transformations
  • Examine relationships
  • Introduction to modeling
  • Improve efficiency

Prerequisites

  • Knowledge of your business requirements

Course Outline

Introduction to IBM SPSS Modeler

  • Introduction to data science
  • Describe the CRISP-DM methodology

Build models and apply them to new data

Collect initial data

  • Describe field storage
  • Describe field measurement level
  • Import from various data formats

Export to various data formats

Understand the data

  • Audit the data
  • Check for invalid values
  • Take action for invalid values

Define blanks

Set the unit of analysis

  • Remove duplicates
  • Aggregate data
  • Transform nominal fields into flags

Restructure data

Integrate data

  • Append datasets
  • Merge datasets

Sample records

Transform fields

  • Use the Control Language for Expression Manipulation
  • Derive fields
  • Reclassify fields

Bin fields

Further field transformations

  • Use functions
  • Replace field values

Transform distributions

Examine relationships

  • Examine the relationship between two categorical fields
  • Examine the relationship between a categorical and continuous field

Examine the relationship between two continuous fields

Introduction to modeling

  • Describe modeling objectives
  • Create supervised models

Create segmentation models

Improve efficiency

  • Use database scalability by SQL pushback
  • Process outliers and missing values with the Data Audit node
  • Use the Set Globals node
  • Use parameters

Use looping and conditional execution

Related Courses

 

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