If we are going to accelerate our amateur data science pursuits, we need to share resources. What I mean by ‘resources’ are all the tools and vendors, open sources, projects and other handy information that we come across, delve into and generally use to get our work done. A lot of it is free. Some are paid. I will make sure to point out these distinctions in order for you to decide what you want to go for.
I am sure you are familiar with a good number of free software and services. My intent is to do a little curation for us ahead of time so you can save time in this pursuit. When you are working with a number of frameworks, tools and packages, it tends to burn the clock and before you know it, you are off track in the primary pursuit chasing down side-allies.
Use the categories list as a filter to see only resources you are interested in.
You will be amazed at the amount of open source and free services that are available to us and what you can get done without spending anything except your time.
We expect to get feedback from the group so we can keep others from going down the rabbit holes. There are lots of them, of which I can personally attest.