Blogs
Data Science Design Pattern #5: Combining Source Variables
Variable selection is perhaps the most challenging activity in the data science lifecycle. Our blog highlights a repeatable approach to variable engineering.
Blogs
Data Science Design Pattern #4: Transformations of Individual Variables
n this post we describe some common ways to transform individual variables, and explore how doing so may benefit an analysis.
Blogs
Data Science Design Pattern #3: Handling Null Values
Most data science algorithms do not tolerate nulls (missing values). So, one must do something to eliminate them, before or while analyzing a data set.
Blogs
Data Science Design Pattern #2: Variable-Width Kernel Smoothing
This data science design pattern blog post focuses on kernel smoothing.
Blogs
Data Science Design Pattern #1: Decision Templates
We hope that this blog will become a clearinghouse within the data science community for these data science design patterns, thereby extending the design-pattern tradition in software development and enterprise architecture to data science.