#
IBM SPSS Modeler Foundations (V18.2)
(0A069G)

# 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

# Prerequisites

- 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

# Course Outline

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