By Damian Mingle
Data Science is built on the interplay between hypothesis-based and pattern-based reasoning. This is an absolute change from traditional analysis at its very core. Pattern-based reasoning and exploratory data analysis afford a means to form or improve hypotheses and discern new analytic paths. In truth, to perform the discovery of significant insights, which is the promise of data science, it is necessary to cultivate the relationship between hypothesis-based and pattern-based reasoning. By making use of both, data science creates a setting where models of reality no longer need to be static and empirically based. Rather, they are continuously verified, updated, and enhanced until superior models are discovered.
These concepts are summarized in the figure below.
The essential exchange between hypothesis-based and pattern-based reasoning is not the solitary difference between traditional analysis and data science. Data science offers a clearly different perspective than capabilities such as business intelligence. It is not the objective of data science to substitute business intelligence functions within an organization, but rather to complement one another. Both data science and business intelligence offer crucial interpretations of an organization’s operations.
Smith, David. “Statistics vs Data Science vs BI.” Revolutions, 15 May 2013.
Damian Mingle is the Director of Data Science for WPC.