Training offering

Amstar Technologies Pvt Ltd

IBM SPSS Modeler Foundations (V18.2)

Information

Length: 16.0 Hours
Course code: 0A069G
Delivery method:

Click on a delivery method to see details

Classroom

Instructor Led Online

Price: 41000 INR

Session dates

Date Location  
Virtual US EST
41000 INR before tax
language: en
Virtual IST
41000 INR before tax
language: en
Virtual US EST
41000 INR before tax
language: en
Virtual IST
41000 INR before tax
language: en
Virtual US EST
41000 INR before tax
language: en
Virtual IST
41000 INR before tax
language: en
Virtual US EST
41000 INR before tax
language: en
Virtual IST
41000 INR before tax
language: en
Virtual US EST
41000 INR before tax
language: en
Virtual IST
41000 INR before tax
language: en

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.

Public

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

Prerequisits

  • Knowledge of your business requirements

Objective

Introduction to IBM SPSS Modeler 
• Introduction to data science 
• Describe the CRISP-DM methodology 
• Introduction to IBM SPSS Modeler 
• 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

Topics

Introduction to IBM SPSS Modeler
• Introduction to data science
• Describe the CRISP-DM methodology
• Introduction to IBM SPSS Modeler
• 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