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.
The self-study e-learning includes:
Who should attendAnalysts, data scientists, and IT professionals looking to craft better inputs to improve predictive modeling performance
This course assumes some experience in both predictive modeling and SAS programming. Before attending this course, you should have:
Familiarity with the SAS macro language is helpful but not required.
This course addresses Base SAS, SAS/STAT software.
Extracting Relevant Data
|Title||Duration||Access Period||Language||Fee||Add to Cart|
|Feature Engineering and Data Preparation for Analytics (PDF + 30 Hours Virtual Lab)||21.0 hours||180 days||English||999 GBP|