I wasn't getting hired as a Data Scientist. So I sought data on who is.

At the time I'm writing this, every single trending article in my Towards Data Science home page is talking about applying or learning a particular skill in data science. At the top are big-picture skills such as How to Work With Stakeholders as a Data Scientist and How to Become a Data Engineer, followed by a litany of very specific skills including technical primers on Batch Gradient Descent vs. Stochastic Gradient Descent, Multi-Class Text Classification, Faster R-CNN, et cetera. As a dedicated Medium platform for "sharing concepts, ideas, and codes" in data science, it is not surprising that such learning resources attain high popularity amongst Towards Data Science followers, who are probably navigating data-centric projects and professions. But to a novice looking to prioritize what is essential, it can quickly become daunting. Should one train to become a master Kaggler?