So I am starting in Data Science and there are two things I am using quite a bit: Machine learning and Django. I have looked throughout the internet and the two worlds seems to be at different ends - ML going with Anaconda/miniconda and Django with PIP.
To me, it makes sense, Anaconda has most of the packages needed for DS and feels a more solid option when using it for those purposes (i.e. data analysis, wrangling, ml modelling, etc). However, for Django, there seems to be everyone (by everyone I mean tutorials and articles on Django) uses PIP. And it makes sense, when deploying a quick app, all you want to do is to create a virtual env, install the needed packages, build the app, pip freeze it and upload to server. Boom.
However, the more I look on the internet, everyone seems to be use either Anaconda/miniconda or PIP. Some stuff I read on Stackoverflow mentions using pip inside Anaconda/miniconda's environment... but for that I would have to create a PIP-virtual-environment inside Anaconda/miniconda's environment.
As you can see, as a newbie, I feel slightly confused with this. As anyone got experience this? What did you do?
