https://github.com/snehilsanyal/executable-books-list
A list of Jupyter books and resources
https://github.com/snehilsanyal/executable-books-list
executable-books jupyter-book
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A list of Jupyter books and resources
- Host: GitHub
- URL: https://github.com/snehilsanyal/executable-books-list
- Owner: snehilsanyal
- Created: 2022-12-20T07:23:35.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-05T09:15:00.000Z (about 3 years ago)
- Last Synced: 2025-02-04T18:52:07.783Z (over 1 year ago)
- Topics: executable-books, jupyter-book
- Homepage:
- Size: 6.84 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# A list of Jupyter Book Resources
## Repos
[Executable Books GitHub Repo](https://github.com/executablebooks/jupyter-book)
[Jupyter Book Themes](https://github.com/QuantEcon/quantecon-book-theme)
## Books
1. [Executable Books Gallery](https://executablebooks.org/en/latest/gallery.html)
2. [Tutorial on Jupyter Books](https://ubc-dsci.github.io/jupyterdays/sessions/beuzen/jupyter_book_tutorial.html)
3. [Foundations of Data Science with Python](https://jmshea.github.io/Foundations-of-Data-Science-with-Python/intro.html)
4. [Python 101](https://python101.ml/intro.html)
5. [Python Packages](https://py-pkgs.org/)
6. [Python Programming for Data Science](https://www.tomasbeuzen.com/python-programming-for-data-science/README.html)
7. [introPy](https://lukas-snoek.com/introPy/)
8. [Graph Pandas](https://pandas.liuzaoqi.com/intro.html)
9. [Prodigious Python](https://prodigiouspython.github.io/ProdigiousPython/intro.html)
10. [Jupyter meets the Earth](https://jupytearth.org/jupyter-resources/ecosystem/jupyterbook.html)
11. [CCA 175 Spark and Hadoop Developer](https://cca175.itversity.com/spark-python/01_getting_started.html)
12. [Teaching and Learning Jupyter](https://jupyter4edu.github.io/jupyter-edu-book/jupyter.html)
13. [Introduction to Material Informatics](https://enze-chen.github.io/mi-book-2021/intro.html)
14. [Subject Matter Authoring using Jupyter Notebooks](https://opencomputinglab.github.io/SubjectMatterNotebooks/intro.html)
15. [Glasgow Lab](https://glasgowlab.org/3_projects.html)
16. [Hands-on Network Machine Learning with Scikit-Learn and Graspologic](http://docs.neurodata.io/graph-stats-book/coverpage.html)
17. [Introduction to Cultural Analytics & Python](https://melaniewalsh.github.io/Intro-Cultural-Analytics/welcome.html)
18. [Neuromatch Acaedemy: Computational Neuroscience](https://compneuro.neuromatch.io/tutorials/intro.html)
19. [Plants and Python](https://plantsandpython.github.io/PlantsAndPython/00_Opening_page.html)
20. [Python for Mathematics](https://vknight.org/pfm/about-this-book/how-is-this-book-written/main.html)
21. [BrainHack Book](http://brainhack.org/brainhack_jupyter_book/)
22. [Sumerian Networks](https://niekveldhuis.github.io/sumnet/welcome.html)
23. [Stats DS Book](https://theoryandpractice.org/stats-ds-book/intro.html)
24. [Scikit-learn Course](https://inria.github.io/scikit-learn-mooc/)
25. [Course on EDA](https://bayesball.github.io/EDA/)
26. [Python Packages](https://py-pkgs.org/) Great resource to learn about Python packages, maintenance and versioning.