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

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 science2. 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 Help3. Introduction to data science using IBM SPSS Modeler• Explain the basic framework of a data-science project• Build a model• Deploy a model4. 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 formats5. Understanding the data• Audit the data• Check for invalid values• Take action for invalid values• Define blanks6. Setting the of analysis• Remove duplicate records• Aggregate records• Expand a categorical field into a series of flag fields• Transpose data7. Integrating data• Append records from multiple datasets• Merge fields from multiple datasets• Sample records8. Deriving and reclassifying fields• Use the Control Language for Expression Manipulation (CLEM)• Derive new fields• Reclassify field values9. 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 fields10. Introduction to modeling• List three types of models• Use a supervised model• Use a segmentation model

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

• It is recommended that you have an understanding of your business data

• Introduction to data science
• Introduction to IBM SPSS Modeler
• Introduction to data science using IBM SPSS Modeler
• Collecting initial data
• Understanding the data
• Setting the of analysis
• Integrating data
• Deriving and reclassifying fields
• Identifying relationships
• Introduction to modeling