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.
This data science design pattern blog post focuses on kernel smoothing.
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.
This post discusses how many data scientists fail to frame business problems as optimizations even after coming to the correct statistical outcome.
Our paper focuses on why enterprise data warehouse projects fail and what to do about it.
In this blog Mosaic discusses how businesses can take advantage of ‘small data’ before taking the plunge of collecting every data available to them.
This case study examines how Mosaic helped NASA and the FAA dynamically balance airspace capacity & demand.
This white paper explores how traditional models of the value of information (VoI) can be extended effectively to account for uncertainties presently inherent in gathering and analyzing big data.
We’ve developed an entire guide on how to stand up a successful data science group.
We built a predictive machine learning model that incorporates weather forecasts and air traffic movements to provide decision support to air traffic controllers.