Anaconda isn’t free anymore, data scientists need to sort their options

Anaconda isn’t free anymore, for commercial use. It was about time to see the drug dealer that just makes UI candy to start charging for the candy that people got addicted to, that we don’t need (installation interface in this case).

Google Collab isn’t free neither. And it lives in the cloud, probably far from your data. And it is just a data exploration tool (as Jupyter), not production environment.

Not to mention that Anaconda is technically problematic and presented issues in many student’s laptops that I’ve seen. Yes, I collected some statistics in large classes. Not to mention that the laptop you’re gonna install Anaconda — probably Windows or Mac — is something very different from production environments — probably Linux or Unix servers — which prevents you from practicing an important DevOps concept called Shift Left that wants to brings the production environment flavor as closest as possible to the developer computer, either as virtual machines or containers. Not to mention that production environments — mostly Linux — have their own Python stack which is much more well integrated to the OS than the alien Anaconda.

So what is the problem of you as a data scientist doing the right thing, getting real Python on a Linux environment and `pip install` everything that you need?

Don’t be lazy, don’t forget who you are. Data scientists are deep technical folks. Programmers. Generating plots for business meetings is just a tiny part of your job. Your real job is to write production code that optimize business where it happens, where data gets born, on servers, on Unix, no UI. To spend the life roaming business meetings is the data analyst job — and it’s an amazingly important job —, it isn’t scientist’s job.

Getting Linux skills is the second most import skill set that data scientists must have, right behind Statistics and Python programming per se.

So don’t be a lazy scientist. People will notice. Learn Linux and barebones Open Source because this is where innovation happens and you want to be close to it.

I'm an experienced hacker focusing lately on Data Science and Data Engineering. I've also done a lot of customer-facing IT activities, programming, teaching etc