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I work at a company where Excel is heavily used as the analytical platform.
However, from my observation nobody even knows anything beyond the top surface
and how to use the first layer Ribbon buttons.
Inspite of that, I see that the top level management is pushing for things like
`qliksense`, and there are discussions of web-based display tools like `Tableau`.
I know there are also other open-source tools like `dash`, `bokeh` as well.
Personally, I have always used Matlab and Python/Linux combination and can
pretty much do anything that is necessary with these tools.
Excel by itself with `COM/.NET` integration and with the help of `Java/Python`
integration can be used as a display layer and communicate with servers running
more computationally intensive applications for ML/AI. Ofcourse Excel is not
the best for ML or storing data of course, but its great for adhoc exploration which is
necessary for exploratory data analysis. Ofcourse, you can do the same with
Jupyter, but in Jupyter you have to actually write python code which business
people don't know how to do, as a result you become dependent on some coder to
do it for you. If you build a few plugins which in turn interact with backend
servers which process and analyze the data, it becomes much easier for
business people to use the applications.
I don't hear too much about `Excel` in the data-science community. So I wonder
why has Excel gone down in popularity?
