The usual progression I've seen in data science is the following:
- Start out learning data analysis with Microsoft Excel
- Switch to a more powerful analysis environment like R or Python
- Look down one's nose at everybody still using Excel
- Come to realize, hey, Excel's not so bad
I'll admit, I was stuck at Step 3 for a few weeks, but luckily I got most of my annoying pooh-poohing (if you're not a native English speaker, that expression might not mean what you think it means) out of my system decades ago when I was a proofreader (hence my nickname, if you were curious).
I think most mature data scientists see Excel as an essential and useful part of the ecosystem; I think the way it brings you so close to your raw data is essential in the early stages to develop data literacy, and later on when you're munging vectors and dataframes it can still be useful to fire up a .csv and have a look-see with no layers of abstraction above it.
Feedback is welcome. I'm not involved with the rest of the Excel course, but I have taken the Complete Web Developer course from Udemy and recommend it. I get absolutely no money for referrals or anything like that (or for page visits for my tutorial for that matter), so this is honest, cross my heart.