Awesome. Fixed that algorithm problem, right?
Wrong.
That’s because algorithms are not the problem… the only problem. The real problem is data preparation. A lot of the examples you’ll read online are very straight forward with nice neat data sets. That’s because they were carefully groomed and prepared. Here I am looking at the wooly wild real data and I’m utterly lost in how to properly prepare this so that it’s appropriately set up as a continuous distribution(or a distribution at all). WOOF! The reason this is so hard is because I actually don’t understand the data fundamentals of the problem I’m trying to solve in exactly the way needed to solve the problem. More cogitation is necessary.
Just because you can write R code doesn’t mean you are a data scientist. Grant has the right mindset, but this post is fair warning that R’s complexity isn’t so much in its being a DSL, but rather in the domain itself.