I've been persistent in #DataCamp's "rate this lesson" and "rate this course" to talk about unnecessary pedanticism, but that's probably the wrong terminology.

An example:
```
SELECT common_name, COUNT(*)
FROM biology
WHERE common_name LIKE '%lady%'
GROUP BY common_name
ORDER BY common_name;
```
gives the same results as

```
SELECT common_name, COUNT(common_name)
FROM biology
WHERE common_name LIKE '%lady%'
GROUP BY common_name
ORDER BY common_name;
```

when there are no nulls (missing values) in the common_name field.

And without some clues in the assignment text, it isn't possible to know which one their JS-based checker will accept. So in the majority of assignments, I get things like this "wrong" in the first pass.

I've lately been vocal about my perception that quality control and beta testing should have exposed such things and led to their correction before deployment to paying customers.

In the case of the #SQL courses, I did recently have a multi-part assignment where they asked for the NOT NULL version from the start, but only in the 3rd or 4th part of the assignment did they expressly say they wanted that and use pre-entered SQL scaffolding to show why it matters in that specific case.

In other news, I've temporarily de-emphasized both #Python and #R-lang (and delayed my exploration of #Julia) because I'm on a "track" that focuses on SQL. At the current rate, I should be finished with the SQL track & emphasis in a month or so.

I've also noticed they have some courses that cover MSExcel / PowerBI, Tableau, Google Sheets, and (of course) "AI". I expect to take the intro courses for most of these topics. I don't typically use spreadsheets except as gridded formatting tools for lists, but many years ago I used Lotus 1-2-3 and Quattro Pro and even took some classes. So relearning such things as formulas and internal scripting may be helpful in making these tools more broadly useful.