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IBM SPSS Modeler Foundations V18.2 0A069G

  • CMDBID 75717
  • Course Code 0A069G
  • Duration 2 Days
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IBM Analytics DS&BA SPSS Course

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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

  • 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

Prerequisites

  • Knowledge of your business requirements

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

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Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Its a cost effective option. One on one training can be delivered too, at reasonable rates.

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SPVC = Self Paced Virtual Class

LVC = Live Virtual Class

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