README: for new contributors to PyMC and/or open source

[reshamas]

We recommend reading through this before contributing to PyMC.

New Contributors to PyMC

Are you new to contributing to PyMC? If so, we have some resources to get you started.

1. Learn more about PyMC. Watch the videos about PyMC and contributing to the library: PyMC Playlist

• Intro to Probabilistic Programming with PyMC (Austin Rochford)
• Intuitive Bayesian Modeling and Computation with PyMC in Python (Oriol Abril Pla)
• Contributing to PyMC (Ricardo Vieira)
• An Example Pull Request to PyMC (Reshama Shaikh)
• Contributing to PyMC Documentation (Oriol Abril Pla)
• Contributing to ArviZ and Open Source: Social and Technical Sides (Oriol Abril Pla)
• Intro to NumPy Array Operations (Meenal Jhajharia)

2. Here is a general video on contributing (etiquette, language, etc) to the scientific python libraries:

• 3 Components of Reviewing a Pull Request

Tips on building skills for GSOC

• Google Summer of Code Experience panel discussion
• Sphinx for Python Documentation
• Software Testing in Open Source and Data Science
• Intro to Unit Testing and Continuous Integration

Suggestions for first-time PyMC contributors

1. Contribute timestamps to videos
   • Do a video timestamp: https://github.com/pymc-devs/video-timestamps
2. 💡 Do a documentation contribution first before doing a code contribution.
   • docstring tutorial: instructions
   • example: Contributing to PyMC Documentation (Oriol Abril Pla)

Tips for second-time PyMC contributors

1. Instructions on setting up your virtual environment: https://pymc-data-umbrella.xyz/en/latest/sprint/tutorials/environment_setup.html
2. Explore the pymc codebase; get to know the code and file structures.

[nagajaideep]

Hi there!

I'm interested in contributing to this project and would love to help improve the README file. Could you please provide some details on what specific changes or improvements are needed? I'd appreciate any guidance or suggestions so I can make the updates accordingly.