Feature Engineering and Data Preparation for Analytics

Target group

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.