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Data Science Design Pattern

Mosaic Data Science, a leading AI Company, has written up a few blogs discussing data science development patterns similar to software development design patterns, which helps accelerate algorithm development and tuning.

Transformations of Individual Variables
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.

By Drew Clancy, 10 yearsMarch 26, 2014 ago
Transformations of Individual Variables
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.

By Drew Clancy, 10 yearsMarch 11, 2014 ago
Transformations of Individual Variables
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.

By Drew Clancy, 10 yearsMarch 3, 2014 ago
Transformations of Individual Variables
Blogs

Data Science Design Pattern #2: Variable-Width Kernel Smoothing

This data science design pattern blog post focuses on kernel smoothing.

By Drew Clancy, 10 yearsFebruary 19, 2014 ago
Transformations of Individual Variables
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.

By Drew Clancy, 10 yearsFebruary 14, 2014 ago
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