Detail kurzu

Feature Engineering and Data Preparation for Analytics

EDU Trainings s.r.o.

Popis kurzu

This course introduces programming techniques to craft and feature engineer meaningful inputs to improve predictive modeling performance. In addition, this course provides strategies to preemptively spot and avoid common pitfalls that compromise the integrity of the data being used to build a predictive model. This course relies heavily on SAS programming techniques to accomplish the desired objectives. The self-study e-learning includes:Annotatable course notes in PDF format. Virtual Lab time to practice.

Obsah kurzu

Extracting Relevant DataData difficulties.Assessing available data.Accessing available data.Drawing a representative target sample.Drawing an uncontaminated input sample.Transforming Transaction and Event DataAdvantages and disadvantages of transactions data.Common transaction structures.Defining the time horizon.Fixed and variable time horizon methods.Implementing common transaction transformations.Using Nonnumeric DataDefinitions and difficulties of nonnumeric data.Miscoding and multicoding detection.Controlling degrees of freedom.Geocoding.Managing Data PathologiesExploring input variable distributions.Detecting data anomalies.Creating custom exploratory tools for candidate input variables.Missing value imputation.Data partitioning.

Cílová skupina

Analysts, data scientists, and IT professionals looking to craft better inputs to improve predictive modeling performance
Certifikát Na dotaz.
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