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IBM SPSS Modeler Foundations (V18.2) Training


What IBM SPSS Modeler Foundations (V18.2) training is all about?

IBM SPSS Modeler Foundations (V18.2) Training is designed to help professionals develop a better understanding of the foundations of IBM SPSS Modeler. This technical training program familiarizes you with data science and explains its practice and principles. It teaches you the techniques to import, prepare, and export data with IBM Modeler v18.2 and helps you gain knowledge about modeling.

Check out the Enterprise Training program, which is perfect for organizations & companies. You will get to learn about collecting initial data, understanding the data, and integrating data. IBM SPSS Modeler Foundations (V18.2) Training teaches you to set the unit of analysis, transform fields and helps you improve efficiency and examine relationships. It is suitable for business analysts and data scientists who want to extend their knowledge base and boost their credibility.

Schedule

Contact us to customize this class with your preferred dates, times and location.
You can call us on 1-800-961-0337 or Chat with our representative.

What are the course objectives for IBM SPSS Modeler Foundations (V18.2) training?
  • Developed effectuality.
  • Elaborating about the modeling.
  • Examining about the relationships.
  • Transformations for further fields.
  • Transformed fields.
  • Integrating the data.
  • Setting and analyzing the units.
  • Understanding the data.
  • Collecting an initial data.
Who should attend IBM SPSS Modeler Foundations (V18.2) training?

This training is recommended for Business Analysts who want to enhance their skills for the same. However, the target audience for this course are data scientists.

What is the course outline for IBM SPSS Modeler Foundations (V18.2) training?
  • 1. Introduction to IBM SPSS Modeler
  • a). Introduction to data science
  • b). Describe the CRISP-DM methodology
  • c). Introduction to IBM SPSS Modeler
  • d). Build models and apply them to new data
  • 2. Collect initial data
  • a). Describe field storage
  • b). Describe field measurement level
  • c). Import from various data formats
  • d). Export to various data formats
  • 3. Understand the data
  • a). Audit the data
  • b). Check for invalid values
  • c). Take action for invalid values
  • d). Define blanks
  • 4. Set the unit of analysis
  • a). Remove duplicates
  • b). Aggregate data
  • c). Transform nominal fields into flags
  • d). Restructure data
  • 5. Integrate data
  • a). Append datasets
  • b). Merge datasets
  • c). Sample records
  • 6. Transform fields
  • i). Use the Control Language for Expression Manipulation
  • a). Derive fields
  • b). Reclassify fields
  • c). Bin fields
  • 7. Further field transformations
  • a). Use functions
  • b). Replace field values
  • c). Transform distributions
  • 8. Examine relationships
  • a). Examine the relationship between two categorical fields
  • b). Examine the relationship between a categorical and continuous field
  • c). Examine the relationship between two continuous fields
  • 9. Introduction to modeling
  • a). Describe modeling objectives
  • b). Create supervised models
  • c). Create segmentation models
  • 10. Improve efficiency
  • a). Use database scalability by SQL pushback
  • b). Process outliers and missing values with the Data Audit node
  • c). Use the Set Globals node
  • d). Use parameters
  • e). Use looping and conditional execution
2 Days | $ 1590
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