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
The Executive Role in a Data-Driven Organization
Our blog post examines the role of an executive in a data-driven organization.
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 Architecture 101, Part 1: Rudiments
This blog aims to become a “nutshell” treatment of the subject, so those of you who work with data in a relational database management system (RDBMS) can quickly learn how to make the best possible use of a database.
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.
Blogs
Sample Size Matters
This post discusses how many data scientists fail to frame business problems as optimizations even after coming to the correct statistical outcome.
Blogs
Small Data, Big ROI
In this blog Mosaic discusses how businesses can take advantage of ‘small data’ before taking the plunge of collecting every data available to them.