Unfortunately, I might be a bad person to answer that question. I'm 52, and have been programming since high school, not as a professional developer, but just using programming as a tool. So I learned data analysis long before learning Python, just through my general science education and work. The result is that I don't really know what a good modern tutorial is.

The old timers would just hand you a copy of Numerical Recipes and wish you good luck. ;-)

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.