I think the same thing is happening in Python for data science due to Pandas. It’s a great package, but many data scientists are only able to manipulate data using Pandas, and know little “base” Python.
I'd like to understand better why the data structures work the way they do and thus have an intuition on what operations to use when. The O'Reilly Python Data Science Handbook[0] seems like it might be useful here, but I'm not sure if it is still up to date.
[0] https://www.oreilly.com/library/view/python-data-science/978...
The OReily book should not be that outdated if at all. It also covers other essential tools.
[0] https://pandas.pydata.org/pandas-docs/stable/getting_started...
In fact python is a terrible language and the only reason anyone should use it is for access to sklearn, scikit, pytorth, and pandas.
Hopefully Julia will be able to unseat python for data analysis in the future.