Target group
Analysts, data scientists, and IT professionals looking to craft better inputs to improve predictive modeling performance
Course structure
Extracting Relevant Data
- Data difficulties.
- Assessing available data.
- Accessing available data.
- Drawing a representative target sample.
- Drawing an uncontaminated input sample.
Transforming Transaction and Event Data
- Advantages and disadvantages of transactions data.
- Common transaction structures.
- Defining the time horizon.
- Fixed and variable time horizon methods.
- Implementing common transaction transformations.
Using Nonnumeric Data
- Definitions and difficulties of nonnumeric data.
- Miscoding and multicoding detection.
- Controlling degrees of freedom.
- Geocoding.
Managing Data Pathologies
- Exploring input variable distributions.
- Detecting data anomalies.
- Creating custom exploratory tools for candidate input variables.
- Missing value imputation.
- Data partitioning.
