From the webpage:
The field of data science is constantly evolving and ever-advancing, with new technologies placing more valuable insights in the hands of modern enterprises. More data-driven organizations are hiring data scientists to drive their efforts to gather, analyze, and make use of Big Data in valuable ways.
Because the field of data science is so broad and sometimes challenging to navigate, we’ve compiled a list of 50 of the most helpful data science resources on the web. Whether you’re a student or new professional working in the field of data science, these resources are valuable for discovering the latest employment opportunities, finding tutorials for the processes and systems you’re using on a daily basis, learning hacks and tricks to boost your performance, and connecting with other professionals in your field.
Note: The following 50 resources are not ranked or rated in order of importance or value; rather, they are categorized to make it easy for you to locate the resources you need most. Click through to a specific category using the links in the Table of Contents below.
A useful list as far as it goes but like all such lists, it probably has resources you have already seen. And the next person who thinks a list of data science resources is a great idea will make yet another list.
I suspect for web based resources, we can do a fair job at deduping lists of resources but how do we create incentives to seek out or make more visible all the existing lists? And of course having done that, how do we create incentives to combine those list together?
So far as I can tell, the nature and extent of incentives for such collaboration are either unknown or unpracticed. I’m betting on unknown. Thoughts on how to explore possible incentives? The worst we can do is remain with the status quo.
I first saw this in a tweet by Marcelo Domínguez.