When you use the term “data scientist” you normally think of an inarticulate introvert who is dazzled by numbers and has week social skills.  Well, this interview with Aaron Pujanandez from Excella may change the preconception. 

We start off with referencing an article from the Harvard Business Journal from 2012. 

This was probably a conceit eleven years ago, but in the last decade we have seen cheap storage, available compute, and ubiquitous fast Internet.  Perhaps the title is getting closer to the truth.

We begin the interview with having Aaron differentiate “data analyst” from “data scientist.”  Many common themes including Python and being part of a team.  From Aaron’s view, a data analyst may be charged with providing a visual depiction of data elements where a data scientist may delve into mor advanced topics like subtleties of Extract, Transform, Load, Machine Language, and code review. 

One of the challenges faced by federal information professionals is the volume of data to ingest.  During the interview, Aaron talked about many of the aspects of selecting data and making sure it is safe in transit.

Aaron provides the listener with his thoughts on selecting the right data, data quality, handling large volumes of data, data access and, finally, the all-important concept of being able to communicate findings to non-technical stakeholders.

There are no silver bullets here – just an opportunity to approach large data sets and artificial intelligence from a perspective that will give actionable results.

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