A Memorandum Giving Thanks (Physics)

From: kirby urner
Date: Fri, Apr 6, 2018 at 12:09 PM
Subject: thank you to physics community for making python
science stack so appealing
To: The Physics Learning Research List

I want to thank the astrophysics and astronomy instrumentation communities for making my life easier as a Python instructor.

They all want to learn Machine Learning these days, and thanks the Physics (umbrella term), the numpy package is up there with MATLAB and free of charge. numpy is the core around which others build.

Hubble instrumentation group had me in as an instructor that time [1] because they knew IDL (their in-house development environment) was too expensive an investment to require of academics just wanting to share the data.

Don’t put all your eggs in proprietary baskets, is the lesson learned from Revolution OS.[2]

Jake Vanderplas, University of Washington, Seattle, has done yoeman’s service as a tireless communicator of scikit-learn skills. His Python Data Science Handbook is free online [3] and well worth owning in hardcopy.

Learning will never be the same now that we have the new tool stacks. Machine Learning has many applications within physics itself of course. I can now better follow the Youtubes.

Thanks again.

[ And to the folks in North Carolina (Sherwood et al — physics teachers) for VPython. I’m used to lavishing effusive praise in that direction, as spatial geometry with Python is my specialty. [4] ]

I’ve only just tackled teaching Machine Learning, so I’m newly appreciative of yet other physics community contributions.



(still a good resource, as it doesn’t overly confuse the advance of copyleft technology with the “dot com” boom and crash in the Clinton Era).

(Jake, if you’re listening, thank you!)


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