So ... #DataCamp.

I decided to finish a couple of #RLang "tracks" before I refocus on the SQL and Python career tracks that I think will be most beneficial, and it has taken much, much longer than I expected.

R and many of its libraries are very inconsistent. But more importantly, few of their R-related courses start at the beginning and lay things out step-by-step. In fact, more than a year after I started with DataCamp was when I first ran into a course that did this. (It was amazing, and so far, I think I've encountered four of them. So finally, "aes" isn't some magic that I have to struggle to remember, it is the aesthetics of a graph / chart.)

So okay, when you take courses at your local community college, they set out the courses for each level based on levels. Learning C? There's an intro to C, followed by Intermediate C (which may be broken into multiple courses and using different names). There may also be an advanced C course. Most of them will have one or more prerequisites, so that you already understand the topics covered by those courses before you take the one you're interested in.

If you're taking the ACS (applied computer science ... may be computer information systems, management information systems, information systems management, information technology, or similar names) program, they'll have a list of which ones are required (which may have prerequisites).

Unfortunately, DataCamp isn't designed that way. It's rather haphazard, with three to fifteen four-hour courses arranged in one of around 30-40 "tracks" that mostly don't have prereqs arranged so that one has / acquires the underlying background before they take a course.

Other: DataCamp has a "pay per year" system which encourages people to take as many courses and tracks as they can and fails to encourage people to take time to do side projects using the skills their courses have covered. It may be good for them: We have X number of users, and most of them complete Y courses per year. It isn't good for their customer / students: No time to grab a few datasets, do some exploratory data analysis, then develop a hypothesis and go through the process to determine whether the dataset(s) support that hypothesis.