IBM SPSS Modeler Foundations (V18.2)

Kurscode 0A069G
Kurssprache Deutsch
Unterlagen Englisch
Anbieter IBM
Preis 1600,00 Euro exkl. USt
Dauer 2 Tage
Kurstermine 1 Termine

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.



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

    • Knowledge of your business requirements


    Ort des Kurses Kursbeginn  
    Nürnberg 06. Dezember 2021 Buchen


    Please refer to course overview.


    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

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