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Yes, it's hard. Mostly because we aren't wired to learn math and engineering the way we are wired to learn things like language.<br />
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It's a difficult process of purposefully hitting a wall, taking a break, and going back to the wall again (and realizing the wall isn't there anymore).<br />
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Everyone learning coding needs to read this book, even if they are not struggling:<br />
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A Mind For Numbers: How to Excel at Math and Science (Even If You Flunked Algebra) 1, Oakley, Barbara - Amazon.com
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<div class="contentRow-snippet js-unfurl-desc">A Mind For Numbers: How to Excel at Math and Science (Even If You Flunked Algebra) - Kindle edition by Oakley, Barbara. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading A Mind For Numbers: How to...</div>
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This will be a very rewarding exercise for anyone learning, though.
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</blockquote>I haven't finished the whole book, but one takeaway I liked was about getting tons of feedback, and considering the various elements of the equations or functions every step of learning process.<br />
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Very relevant with my Forecasting unit in college, where I'm learning R.<br />
It was great to have tutorial sessions full of playing around with sample datasets and forecast method codes. I also used Datacamps' daily practices (works like flashcards) to drill down the fundamental bits.<br />
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We had two coding projects throughout the semester, where we would take a data set from some online source, run it through some forecast models, and write up some policy recommendations.<br />
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As the weeks when by, we would learn various concepts like training-test sets, cross-validation, exponential methods, and our lecturer and tutors would point out lots of deficiencies in our code or report approaches, from the general consultation sessions.<br />
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Very very vigorous, but I found it nice that I could learn new things every week.</div>
