IBM SPSS Modeler Fundamentals


IBM SPSS Modeler offers the predictive power of proven statistical algorithms combined with the usability and customizability of an intuitive graphical user interface. SPSS Modeler enables users to proactively add predictive analytics and data mining capabilities to their organization. As part of our valuable curriculum of IBM SPSS training, the Ironside Group provides instructor-led classes that introduce users to the capabilities of SPSS Modeler and give them the contextual information and tools necessary to begin their journey into data mining and predictive analytics.



Course Outline

This two-day class is suited for users from any industry who are interested in understanding the basics of how to create predictive models using IBM SPSS Modeler and who want a solid introduction to data mining and predictive analytics as a whole.

During the Introduction to SPSS Modeler class, students will learn the essentials of data mining and predictive analytics, explore the Modeler interface, and work with Modeler functions, including creating predictive models, manipulating data files, and checking data quality.

Each topic will be highlighted with in-depth demos, Q&A sessions, and tips and techniques drawn from over 20 years of cross industry experience.


The curriculum is based on the most recent version of IBM SPSS Modeler, and uses the sample data included in the Demo folder. For an even more enriching experience, we can customize course content to any customer’s environment, including but not limited to the tutorials and workshops.

All our instructors have over 20 years of real-world experience with SPSS Modeler, advanced statistical concepts, and predictive analytics in general.


A good comprehension of Microsoft Windows and the Office suite is mandatory.

Some understanding of statistical concepts is beneficial.

High-level Curriculum

  • Basics of Data Mining
  • Basics of IBM SPSS Modeler
  • Implementing a Data Mining Project
  • Reading Raw Data
  • Defining a Record for Analysis
  • Integrating Data
  • Exploring Data
  • Data Manipulation and Transformation
  • Testing for Relationships
  • Types of Modeling Projects