Re:GNU Octave
by LolWolf on Saturday November 19, 2016 @03:14PM
(#12995363)
It depends on what kind of analysis you're doing. If you're doing pure exploratory stuff on large datasets, you'll mostly be using NumPy as a tool to help with Pandas. See McKinney's Python for Data Analysis (I believe it's from O'Reilly books) if you'd like a fairly good intro.
If you're more like me on using numerical methods, then I'd recommend first going through the math (I can be more specific, depending on what kind of work you'd like to do/are interested in) using Julia/MATLAB/Octave and then jump into NumPy stuff.
In my experience, I've found that using NumPy/SciPy numerical libraries effectively requires some knowledge of the underlying numerical algorithms since the default arguments sometimes require tweaking.
If you're more like me on using numerical methods, then I'd recommend first going through the math (I can be more specific, depending on what kind of work you'd like to do/are interested in) using Julia/MATLAB/Octave and then jump into NumPy stuff.
In my experience, I've found that using NumPy/SciPy numerical libraries effectively requires some knowledge of the underlying numerical algorithms since the default arguments sometimes require tweaking.