This course covers advanced topics to aid in the preparation of data for a successful data science project. You will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.
Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and basic knowledge of modeling. Prior completion of the Introduction to IBM SPSS Modeler and Data Science course is recommended.
This advanced course is intended for anyone who wants to become familiar with the full range of techniques available in IBM SPSS Modeler for data preparation.
Using functions to cleanse and enrich dataUsing additional field transformationsWorking with sequence dataSampling, partitioning and balancing dataImproving efficiency, Cloud Data and AI, Information Architecture Market, AI Tools Decision Optimization