Have you ever been excited to try a new software package only to end
up frustrated after spending hours getting it to install correctly?
How often do you return to a project from a few months ago and the
code is broken due to software updates? Do you and your collaborators
struggle to run each others' code on your own computers? Is your
GitHub Issue tracker full from users unable to install your software
on operating systems that you are unfamiliar with? Do you ever wish
you could test out the latest version of a software package without
affecting your current setup? If you've ever faced any of these
challenges, there is a better way to install and manage scientific
software across your various projects. Conda (https://conda.io/docs/)
is a cross-platform, language-agnostic package management system and
environment management system. And it's not just for Python packages!
Conda can also manage R packages and many other dependencies you might
need for your data science projects. I will cover how conda compares
to other package and environment management systems, how to setup
conda and install packages, how to create isolated computational
environments for individual projects, and how to build and share your
own conda packages.