No, as the post was about the fundamental and irreconcilable
differences in capabilities, not the incidental ones that can be
solved if folks choose (or are paid) to put in the necessary design
and development time.

The post isn't written for beginners deciding which tool to use, it's
written for intermediate folks that have already chosen one or the
other for their own needs, and are wondering why the other still
exists (or, worse, are telling people that have chosen the other tool
for good reasons that they're wrong, and should switch).

- you need a system for specifying environmental *constraints* (like
dynamically linked C libraries and command line applications you
invoke)
- you need a system for asking the host environment if it can satisfy
those constraints

dnf/yum, apt, brew, conda, et al all *work around* the current lack of
such a system by asking humans (aka "downstream package maintainers")
to supply the additional information by hand in a platform specific
format.

> But the different audiences aren't "data science folks" vs "web developers"
> -- the different audiences are determined by deployment needs, not domain.

Deployment needs are strongly correlated with domain though, and
there's a world of difference between the way folks do exploratory
data analysis and the way production apps are managed in
Heroku/OpenShift/Cloud Foundry/Lambda/etc.

 However, if you're specifically interested in web service
development, then swapping in your own Python runtime rather than just
using a PaaS provided one is really much lower level than most
beginners are going to want to be worrying about these days - getting
opinionated about that kind of thing comes later (if it happens at
all).