[frabbit]

There are at least two big turn-offs to this course at first blush: 1) they insist on using anaconda (effectively another package manager complicating the already layered interaction of system pip, virtualenv, virtualenvwrapper etc ). 2) they use Microsoft VisualStudioCode (so, inevitably a good deal of time in this course will be spent learning how to navigate a bloated IDE)

[pc86]

Neither of which are the least bit consequential for anyone more interested in learning about data science.

[frabbit]

As it turns out I was wrong about one of those points: in fact the course prefers that you avoid MSVisualStudioCode and instead use the Jupyter notebooks.

But, this bring us back to a much more central topic in data science: the tools and environment DO matter. Hugely.

Reproducibility is central to not just data science but all science. This is facilitated by the use of Free, Open platforms which adhere to common standards.

Imagine trying to debug why someone has a different answer than you when there are x*variant-of-program environments in which they have obtained their answer?

At the most basic level this course should be distributing a Docker image or a VM image of some sort in order to ensure that everyone has the same version of the software.

Even if you do not care about any of the above, please, shed a tear for the student who would like a simple setup.

Thank you.