The thin line between data science and data engineering

 Editor’s note: This is the fourth episode of the Towards Data Science podcast “Climbing the Data Science Ladder” series, hosted by Jeremie Harris, Edouard Harris and Russell Pollari.

Together, they run a data science mentorship startup called SharpestMinds.

You can listen to the podcast below:   If you’ve been following developments in data science over the last few years, you’ll know that the field has evolved a lot since its Wild West phase in the early/mid 2010s.

Back then, a couple of Jupyter notebooks with half-baked modeling projects could land you a job at a respectable company, but things have since changed in a big way.

Today, as companies have finally come to understand the value that data science can bring, more and more emphasis is being placed on the implementation of data science in production systems.

And as these implementations have required models that can perform on larger and larger datasets in real-time, an awful lot of data science problems have become engineering problems.

That’s why we sat down with Akshay Singh, who among other things has worked in and managed data science teams at Amazon, League and the Chan-Zuckerberg Initiative (formerly Meta.


Akshay works at the intersection of data science and data engineering, and walked us through the fine line between data analytics and data science, the future of the field, and his thoughts on best practices that aren’t getting enough love.

Here were our key take-homes:   If you’re on Twitter, feel free to connect with me anytime @jeremiecharris!  Original.

Reposted with permission.

Related: var disqus_shortname = kdnuggets; (function() { var dsq = document.

createElement(script); dsq.

type = text/javascript; dsq.

async = true; dsq.

src = https://kdnuggets.



js; (document.

getElementsByTagName(head)[0] || document.


appendChild(dsq); })();.. More details

Leave a Reply