Introduction to IBM SPSS Modeler and Data Science (v18.1.1)

Instructors
David Cutajar BA (Hons)MSc
Duration
2 Days
Course Level
LearnQuest / IBM Approved Certification
Requirements
A basic understanding of business data is recommended
Certification
LearnQuest / IBM Approved Certification

 

Introduction to IBM SPSS Modeler and Data Science 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.

 

Who is it for?

This course is aimed for business analysts, data scientists and individuals who are new to IBM SPSS Modeler or want to find out more about using it.

 

What should I have?

An understanding of your business data is recommended.

 

 

Our trainer is an experienced IBM-SPSS Statistics and DataMining Consultant

Refreshments and course notes included

 

 

Course 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 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

 

 

*Disclaimer: Kindly note the scheduled dates below are tentative and are therefore subject to change. Please, do register your interest as we are taking provisional bookings. 

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