#11. AstroML

Last edited on 2023-04-28 Tagged under  #space 

Here are this week's 3 links worth exploring:

  1. AstroML is a Python module for machine learning and data analysis built on numpy, scipy, scikit-learn, matplotlib, and astropy. The project's focus is analyzing astronomical data, providing a suite of tools and examples to study open astronomical datasets. Its paired with a textbook - Statistics, Data Mining, and Machine Learning in Astronomy: https://www.astroml.org/

  2. A tutorial on how to make a (stunning!) map of the solar system - including all known asteroids and comets - using open-source code and data from NASA: https://github.com/eleanorlutz/asteroids_atlas_of_space

  3. Transform to Open Science (TOPS) is a NASA initiative to partner with the scientific community to encourage greater access and participation by the public in open science. An open science curriculum, prizes, and hackathons is being developed: https://science.nasa.gov/open-science/transform-to-open-science

Quote of the Week: "Musk differed from his competitors in another, important way — failure was an option. At most other aerospace companies, no employee wanted to make a mistake, lest it reflect badly on an annual performance review. Musk, by contrast, urged his team to move fast, build things, and break things. At some government labs and large aerospace firms, an engineer may devote a career to creating stacks of paperwork without ever touching hardware. The engineers designing the Falcon 1 rocket spent much of their time on the factory floor, testing ideas, rather than debating them. Talk less, do more." — Eric Berger, Liftoff: Elon Musk and the Desperate Early Days That Launched SpaceX


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