Overview

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 fundamentals 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 v18.1.1, 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

Audience

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

Prerequisites

• It is recommended that you have an understanding of your business data

Objective

Please refer to course overview

Course Outline

1. Introduction to data science
• List two applications of data science
• Explain the stages in the CRISP-DM methodology
• Describe the skills needed for data science
2. Introduction to IBM SPSS Modeler
• Describe IBM SPSS Modeler's user-interface
• Work with nodes and streams
• Generate nodes from output
• Use SuperNodes
• Execute streams
• Open and save streams
• Use Help
3. Introduction to data science using IBM SPSS Modeler
• Explain the basic framework of a data-science project
• Build a model
• Deploy a model
4. Collecting initial data
• Explain the concepts "data structure", "of analysis", "field storage" and "field measurement level"
• Import Microsoft Excel files
• Import IBM SPSS Statistics files
• Import text files
• Import from databases
• Export data to various formats
5. Understanding the data
• Audit the data
• Check for invalid values
• Take action for invalid values
• Define blanks
6. Setting the of analysis
• Remove duplicate records
• Aggregate records
• Expand a categorical field into a series of flag fields
• Transpose data
7. Integrating data
• Append records from multiple datasets
• Merge fields from multiple datasets
• Sample records
8. Deriving and reclassifying fields
• Use the Control Language for Expression Manipulation (CLEM)
• Derive new fields
• Reclassify field values
9. Identifying relationships
• Examine the relationship between two categorical fields
• Examine the relationship between a categorical field and a continuous field
• Examine the relationship between two continuous fields
10. Introduction to modeling
• List three types of models
• Use a supervised model
• Use a segmentation model