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True, very true and you'll hit that language barrier in no time. The courses, mostly, provide hands-on experience and don't explain, say for example, what does standard deviation mean?

But this is a good news/bad news kind of thing. Bad news, you need some statistics education to make a sense of what your computer is telling you. Good news, that mathematics isn't "high-grade" mathematics involving integrals and other stuff (as far as I can see anyway).

So what I am doing as a person who is trying to convert himself into a Data scientist is I am taking a basic udemy course (https://www.udemy.com/python-for-data-science-and-machine-le...) to give me the basics and a book on statistics to properly grok what I am seeing on the screen. On this part I am following a different book than its peers though, so it might not be for you but I like the story-telling aspect of it (https://www.amazon.com/Adventure-Statistics-Reality-Enigma/d...)

After I finish those I'll move to Kirill Eremenko's a-z courses on Machine Learning, AI and Deep Learning. I've found that even though they teach cool tricks and some basics before going in detail, that basics part didn't contain enough information for me as a new student. So I feel if you have some proper background in stats and python data analysis you can skip the parts I mentioned and go straight to a-z courses.

Thanks for your insights. I was wondering if one also needs a footing in vector calculus / linear algebra / integrals and other such words I have no idea about.

Also Kirill Eremenko has 38 courses listed. In what order should one take them?

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