Feature Engineering and Data Preparation for Analytics
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.
Virtual Training nebo e-Learning?
Máme dostatečnou flexibilitu, takže vybírat můžete jak prezenční termíny, tak online kurzy.
Zkuste živý kurz virtuálněTarget 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.
Prerequisites
This course assumes some experience in both predictive modeling and SAS programming. Before attending this course, you should have:
- Exposure to DATA step programming equivalent to the SAS Programming 1: Essentials course.
- Exposure to programming in SQL or the SQL procedure.
- Exposure to querying data in PROC SQL and building and deploying a predictive model.
- Familiarity with the analytical process of building predictive models and scoring new data.
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Jsme vám k dispozici na telefonním čísle +420 222 553 101 vždy od pondělí do pátku: 9:00 - 17:00.
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