Our blogs are your gateway to a world of thought-provoking articles that navigate the complexities of AI, machine learning, and data-driven strategies. Authored by our team of data science experts, each blog post unveils a tapestry of knowledge, demystifying intricate concepts, unraveling industry trends, and offering fresh perspectives on the ever-evolving landscape of technology.
In software-development & data architecture nirvana, the business analysts, database technologists, and application developers all speak the same language. Everyone agrees about what each user story means.
Data marts, data warehouses, and some operational datastores use dimension tables. A dimension table categorizes a fact table that joins to the dimension. At query time one filters the facts by values in the dimension table, and uses those values to label the query results
This post uses those concepts to survey the main types of relational architectures. These divide fundamentally into two types, the second having four sub-types: OLTP & BI.
In this post we explain why the assumption about industry experience is outdated—why often industry experience detracts from the best possible application of data science.
Variable selection is perhaps the most challenging activity in the data science lifecycle. Our blog highlights a repeatable approach to variable engineering.
n this post we describe some common ways to transform individual variables, and explore how doing so may benefit an analysis.
Our blog post examines the role of an executive in a data-driven organization.
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.
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.