Still working on finishing the track on #DataCamp. But I wanted to add a little more to this.

It took me most of a year to discover this, but I struggled mightily with data analysis functions in #Python + #Numpy + #Pandas, in #R-lang, and in #Julia-lang. #SQL was much easier to comprehend. But I've recently had a few courses where they were covering pure Python, without the data analysis packages, and that is totally different.

Even though I've barely touched Python in the past 20 years or so, it feel familiar and almost everything we do feels "natural". With the data analysis / data science content, it feels like there are dozens of nearly identically-named functions and methods, each with its own special syntax and list of arguments to pass to it.

fleep(ugarit=1, dopongo='nezhir', neeq=['bijoc', 'umbagula'])

and

floop(nsommus=17, dubunoid=['nezhir', 5, 'immertel'], neeq=['bijoc', 'umbagula'])

are easily mixed up and I always (no, seriously always) pick the wrong one first.

I guess that's not a DataCamp issue, but more of a problem with the tools being covered.

But DataCamp's methods don't help with this much. Each one-hour chapter of each four-hour course is supposed to be a sequence of bite-sized tools that one learns to use and then remembers it when it comes up again later. Unfortunately, it quickly turns into a big ball of mud.