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0A008G - Introduction to IBM SPSS Modeler and Data Science v18.1.1

Overview

Duration: 2.0 days
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.

Objectives

  • Introduction to data science
  • Introduction to IBM SPSS Modeler
  • Introduction to data science using IBM SPSS Modeler
  • Collecting initial data
  • Understanding the data
  • Setting the of analysis
  • Integrating data
  • Deriving and reclassifying fields
  • Identifying relationships
  • Introduction to modeling

Audience

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

Content

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 Modelers 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

Prerequisites

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

Certification

This course is not associated with any Certification.

Schedule




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