Introduction to IBM SPSS Modeler and Data Science (v18.1.1) SPVC - 0E008G

Description

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

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

Aim

Please refer to course overview

Participant requirements

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

Participant

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