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.
Please refer to course overview
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.
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.
1: Using functions to cleanse and enrich data
Use date functions
Use conversion functions
Use string functions
Use statistical functions
Use missing value functions
2: Using additional field transformations
Replace values with the Filler node
Recode continuous fields with the Binning node
Change a fields distribution with the Transform node
3: Working with sequence data
Use sequence functions
Count an event across records
Expand a continuous field into a series of continuous fields with the Restructure node
Use geospatial and time data with the Space-Time-Boxes node
4: Sampling, partitioning and balancing data
Draw simple and complex samples with the Sample node
Create a training set and testing set with the Partition node
Reduce or boost the number of records with the Balance node
5: Improving efficiency
Use database scalability by SQL pushback
Process outliers and missing values with the Data Audit node
Use the Set Globals node
Use looping and conditional execution