Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

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

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

• Knowledge of your business requirements

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