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https://github.com/mpi-astronomy/awesome-astro-datascience

List of resources for Astronomy Data Science
https://github.com/mpi-astronomy/awesome-astro-datascience

List: awesome-astro-datascience

awesome-list data-science machine-learning

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List of resources for Astronomy Data Science

Host: GitHub
URL: https://github.com/mpi-astronomy/awesome-astro-datascience
Owner: mpi-astronomy
License: bsd-3-clause
Created: 2022-04-03T19:54:33.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-01-23T11:45:31.000Z (11 months ago)
Last Synced: 2024-05-19T22:42:27.396Z (7 months ago)
Topics: awesome-list, data-science, machine-learning
Homepage:
Size: 39.1 KB
Stars: 10
Watchers: 2
Forks: 2
Open Issues: 1
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

ultimate-awesome - awesome-astro-datascience - List of resources for Astronomy Data Science . (Other Lists / Monkey C Lists)

README

        # List of resources for Astronomy Data Science [![awesome][awesome-badge]][awesome-link]

An [awesome list](https://github.com/sindresorhus/awesome) of resources for astronomers interested in Data Science.  *Everyone* is invited to [contribute](CONTRIBUTING.md) by pull request.

## Table of Contents

- [Books](#Books)

- [Blogs](#Blogs)

- [Twitter](#twitter)

- [Popular Websites](#popular-websites)

- [Courses](#Courses)

- [Repositories](#Repositories)

- [Other](#Other)

- [Other Awesome Lists](#other-awesome-lists)

- [Contribute](#contribute)

- [License](#license)

### Books

- [Modern Statistical Methods for Astronomy](https://astrostatistics.psu.edu/MSMA/) by Eric D. Feigelson & G. Jogesh Babu. R scripts and datasets available for download from the website.

- [Statistics, Data Mining, and Machine Learning in Astronomy](https://github.com/astroML/astroML-notebooks) by Željko Ivezić, Andrew Connolly, Jacob Vanderplas, and Alex Gray

- [Python for Astronomers](https://github.com/prappleizer/prappleizer.github.io) by Imad Pasha and Chris Agostino. Also [interactive webpage](https://prappleizer.github.io/) and [textbook](https://prappleizer.github.io/textbook.pdf).

- [Machine Learning for Physics and Astronomy](https://press.princeton.edu/books/paperback/9780691206417/machine-learning-for-physics-and-astronomy) by Viviana Acquaviva. Notebooks and slide decks with resources available for download under the "Resources" tab. 

### Blogs

- [AAS Policy Blog](http://aas.org/policy/policy-blog) Josh Shiode

- [AstroBetter](http://www.astrobetter.com/) Kelle Cruz, Joanna Bridge and many other contributors

- [astrobites](https://astrobites.org/) The astro-ph reader's digest

- [AstroWright](http://sites.psu.edu/astrowright/) Jason Wright

- [Hogg Blog](http://hoggresearch.blogspot.com/) David W. Hogg

- [gully](http://gully.github.io/blog/) Michael Gully-Santiago

- [Statistical Modeling Columbia](https://statmodeling.stat.columbia.edu/) Andrew Gelman et al.

- [Xi'An' OG](https://xianblog.wordpress.com/) Christian Robert

- [Statistical Thinking](https://www.fharrell.com/#posts) Frank Harrell

- [Statisticians React to the News](https://blog.isi-web.org/react/)

- [Robert Kosara](https://eagereyes.org/) Visualization & communication

- [Junk Charts](https://junkcharts.typepad.com/) Kaiser Fung

- [Multiple Views](https://medium.com/multiple-views-visualization-research-explained) visualization research explained

### Twitter

People who work in this space and are occasionally active on Twitter: 

- [Daniela Huppenkothen](https://twitter.com/Tiana_Athriel)

- [Dustin Lang](https://twitter.com/dstndstn)

- [Gautham Narayan](https://twitter.com/gsnarayan)

- [Jake VanderPlas](https://twitter.com/jakevdp)

- [John Wu](https://twitter.com/jwuphysics)

- [Joshua Bloom](https://twitter.com/profjsb)

- [Josh Peek](https://twitter.com/jegpeek)

- [Marc Huertas-Company](https://twitter.com/MHuertasCompany)

- [Michelle Ntampaka](https://twitter.com/astro_michelle)

- [NOIRLab DataLab](https://twitter.com/DataLabAstro)

- [Peter Melchior](https://twitter.com/peter_melchior)

- [Viviana Aquaviva](https://twitter.com/AstroVivi)

### Popular websites

In the following, be critical, not all articles are written by specialists. Some are also experiements and others are just for fun.

- [Towards data science](https://towardsdatascience.com/)

- [Medium/Data Science](https://medium.com/tag/data-science)

- [Techradar](https://www.techradar.com/pro)

- [devblogs/python](https://devblogs.microsoft.com/python/) Formerly Planet Python

- [KDnuggests](https://www.kdnuggets.com/) Machine Learning, Data Science, Big Data, Analytics, AI.

- [Machine Learning Mastery](https://machinelearningmastery.com/blog/) by Jason Brownlee

- [Unofficial Google Data Science](https://www.unofficialgoogledatascience.com/)

- [TechXplore/ML](https://techxplore.com/machine-learning-ai-news/)

### Courses

- [Analytics Vidhya](https://www.analyticsvidhya.com/)

- [Google ML Crash Course](https://developers.google.com/machine-learning/crash-course)

### Repositories

#### Libraries

- [Astronomaly](https://github.com/MichelleLochner/astronomaly) A flexible framework for anomaly detection in astronomy.

#### Tutorials

- [Rubin Observatory Tutorial Jupyter Notebooks for Data Preview 0](https://github.com/rubin-dp0/tutorial-notebooks)

- STScI [general Jupyter Notebooks](https://github.com/spacetelescope/notebooks) 

- STScI [JWST Jupyter Notebooks](https://github.com/spacetelescope/jdat_notebooks) showcasing pipeline and analysis tools via science use cases

- [Tutorials for creating figures, tables, or other content](https://github.com/AASJournals/Tutorials) by AAS Journals

#### Course and Workshop Materials

- Astro Hack Week tutorial materials: [2015](https://github.com/AstroHackWeek/AstroHackWeek2015), [2016](https://github.com/AstroHackWeek/AstroHackWeek2016), [2017](https://github.com/AstroHackWeek/AstroHackWeek2017), [2018](https://github.com/AstroHackWeek/AstroHackWeek2018), [2019](https://github.com/AstroHackWeek/AstroHackWeek2019), [2020](https://github.com/AstroHackWeek/AstroHackWeek2020). Also see YouTube for recordings of some events: [2015](https://www.youtube.com/watch?v=BBDCCvY9knI&list=PLFyFNCb8irhOjeD9G7e4myw6Ot7DaBk2W), [2016](https://www.youtube.com/watch?v=EjnR_Ehz-9M&list=PLKW2Azk23ZtQSHmwOpObPEr58Pe1rpIdB), [2020](https://www.youtube.com/user/SimonsFoundation/search?query=%22Astro%20Hack%20Week%22)

- [Code/Astro Workshop Workshop materials](https://github.com/semaphoreP/codeastro) by Jason Wang. A Software Engineering Workshop for Astronomy. 

- [ESCAPE data science summer school 2021](https://github.com/escape2020/school2021) Materials on software development and open science by the European Science Cluster of Astronomy & Particle physics ESFRI research infrastructures project.

- [Foundations of Astronomical Data Science](https://datacarpentry.org/astronomy-python/) Carpentries Curriculum

- [Kavli 2019 Summer Program in Astrophysics Lectures](https://github.com/dkirkby/kavli2019) by David Kirkby. Machine Learning in the era of large astronomical surveys.

- [GROWTH Astronomy School 2019](https://www.growth.caltech.edu/growth-astro-school-2019-resources.html): a school on multi-messenger time domain astronomy

- [Machine Learning and Statistics for Physicists](https://github.com/dkirkby/MachineLearningStatistics) by David Kirby. Material for a UC Irvine course offered by the Department of Physics and Astronomy.

- [Analytical Methods and Applications to Astrophysics and Astronomy](https://www.youtube.com/watch?v=SXPdI_P0_cQ&list=PLUG23R), Statistical and Applied Mathematical Sciences Institute (SAMSI), 2016

- [Time Series Methods for Astronomy](https://www.youtube.com/watch?v=chcpop1a-g8&list=PLUG23RFb_6KftdxAP6e0IRbSlnojX5Zq9), Statistical and Applied Mathematical Sciences Institute (SAMSI), 2017

- [Big Data Physics: Methods of Machine Learning](https://github.com/gtrichards/PHYS_440_540) by Gordon Richards at Drexel University; lots of useful links in the readme.

- [Astrostatistics and Machine Learning class for the MSc degree in Astrophysics at the University of Milan-Bicocca](https://github.com/dgerosa/astrostatistics_bicocca_2024) by Davide Gerosa

- [Machine Learning for Physics and Astronomy (2022-2023)](https://github.com/LHCfitNikhef/ML4PA) by Juan Rojo, Tanjona Rabemananjara and Ryan van Mastrigt

- [Big Data in Astrophysics, Spring 2023](https://github.com/mcoughlin/ast8581_2023_Spring) by Michael Coughlin and Jie Ding, University of Minnesota

- [ASTR 596: Fundamentals of Data Science, Spring 2023](https://github.com/gnarayan/ast596_2023_Spring) by Gautham Narayan, University of Illinois Urbana Champaign

- [Astrostatistics, Vanderbuilt, Spring 2022](https://github.com/VanderbiltAstronomy/astr_8070_s22) by Stephen R. Taylor

- [La Serena School for Data Science, 2023](http://lssds.aura-astronomy.org/winter_school/content/2023-final-program)

#### GitHub Orgs

- [Astronomy Commons GitHub Org](https://github.com/astronomy-commons) Software Infrastructure for Science Platforms and Scalable Astronomy on Cloud Resources

- [NOIRLab DataLab GitHub Org](https://github.com/astro-datalab) Resources for working with the NOIRLab archives, including Jupyter Notebooks.

- [SDSS](https://github.com/sdss)

### Other

- [Deep Skies](https://deepskieslab.com/) A community that fosters knowledge transfer for the accelerated application of artificial intelligence to astronomical challenges.

- [ML Club](https://docs.google.com/document/d/1GGtE-YIuAWlmpKSr38_kyiF-Fklszhkh4FkiYWzBAho/pub) An online discussion on Machine Learning

for astrophysicists. Slides and recordings of events from 2018 to 2021. Currently on hiatus.

## Other Awesome Lists

* [awesome-datascience](https://github.com/academic/awesome-datascience)

* [awesome-astronomy](https://github.com/jonathansick/awesome-astronomy) by Jonathan Sick

* [awesome-awesome](https://github.com/emijrp/awesome-awesome)

* [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness) per coding language

* [sindresorhus/awesome](https://github.com/sindresorhus/awesome) The original

* [The Warren](https://github.com/torchhound/warren)

## Contribute

Contributions welcome! Read the [contribution guidelines](CONTRIBUTING.md) first.

## License

[![CC0][CC0-badge]][CC0-link]

See [LICENSE](LICENSE).

[awesome-badge]: https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg

[awesome-link]: https://github.com/sindresorhus/awesome

[CC0-badge]: http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg

[CC0-link]: https://creativecommons.org/publicdomain/zero/1.0/