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