Three weeks ago, I had the pleasure of attending the Domino Data Science Pop-up, which was…
Data / AI / ML
Last week, I shared some lessons learned from a Domino Data Science Pop-up that I attended a month ago. There were some very important discussions surrounding the world of data science today. One thread explored the differences between data science and data engineering. I’ll admit that I was completely unaware of the engineering behind data science when we […]
Three weeks ago, I had the pleasure of attending the Domino Data Science Pop-up, which was…
A few weeks ago, I attended Rev, a summit for data science leaders and practitioners organized…
Last week, I shared some lessons learned from a Domino Data Science Pop-up that I attended a month ago. There were some very important discussions surrounding the world of data science today. One thread explored the differences between data science and data engineering.
I’ll admit that I was completely unaware of the engineering behind data science when we first launched Insight Data Science back in 2012. And I don’t believe that I was alone. We data scientists were too enamoured with the idea of having the sexiest job of the 21st century.
However, quietly under our radar, data engineering (our “slightly younger sibling”) was emerging, stretching its wings, and undergoing its own evolution. You can read about this from Maxime Beauchemin, data engineer at Airbnb).
So, what is the difference between a data scientist and data engineer? Companies often overlap these positions but understanding the distinction is essential to building your team and hiring the right resources.
Since Insight added a Data Engineering Program in recent years, we can compare it to the Data Science program to shed some light on these two important roles.
Data Science
The key responsibilities for a data scientist are:
Data scientists have a PhD because “it demonstrates that s/he has spent roughly 5 intense years in graduate training to either ask the right questions about data, performing data analysis, create statistical or mathematical models, and present results.”
Data Engineering
A good data engineer:
Data engineers should have very strong software engineering skills. They need to be able to quickly learn to use any of the big data tools on the market, as well as be able to improve the available tools if needed.
With all that said, the easy way to look at the two roles: data engineers enable data scientists to do their jobs more effectively.
So, for those of you looking to build out your data science team: before you hire your first data scientist, ask yourself, will he or she have the infrastructure to be successful? It just might be that you need to hire a data engineer first.
Version One
It’s been about a little over a month since I joined Version One and returned to early-stage venture after spending the past five years as a founder in the addiction treatment space. While a month is a short amount of time, it’s been fascinating to see how certain things have changed during my time away. […]
The first week of September is my VC anniversary. This milestone is always a great…
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