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Python and Machine Learning in Astronomy

Episode #81, published Fri, Oct 21, 2016, recorded Fri, Oct 21, 2016.

This episode is carbon neutral.
The advances in Astronomy over the past century are both evidence of and confirmation of the highest heights of human ingenuity. We have learned by studying the frequency of light that the universe is expanding. By observing the orbit of Mercury that Einstein's theory of general relativity is correct.

It probably won't surprise you to learn that Python and data science play a central role in modern day Astronomy. This week you'll meet Jake VanderPlas, an astrophysicist and data scientist from University of Washington. Join Jake and me while we discuss the state of Python in Astronomy.

Links from the show:

Jake on Twitter: @jakevdp
Jake on the web: staff.washington.edu/jakevdp
Python Data Science Handbook: shop.oreilly.com/product/0636920034919.do
Python Data Science Handbook on GitHub: github.com/jakevdp/PythonDataScienceHandbook
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data: press.princeton.edu/titles/10159.html
PyData Talk: youtube.com/watch?v=qOOk6l-CHNw
eScience Institue: @UWeScience
Large Synoptic Survey Telescope: lsst.org
AstroML: Machine Learning and Data Mining for Astronomy: astroml.org
Astropy project: astropy.org
altair package: pypi.org/project/altair

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Jake VanderPlas
Jake VanderPlas
Jake VanderPlas is a Senior Data Science Fellow at University of Washington’s eScience institute. His background is in Astronomy, and apart from his own research and writing, he spends much of his time developing, maintaining, and training users of the open software tools that are increasingly important to researchers in today’s data-centric world.
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