My name is Prem Kumar Shaw, and I’m an aspiring open-source contributor interested in Bayesian modeling and Data Science. I’ve been learning Python for a while and recently started exploring probabilistic programming.
I came across PyMC through the NumFOCUS projects list and was impressed by how it simplifies Bayesian inference in Python. I’m especially interested in learning more about [mention one area you like, e.g., time-series modeling, state-space models, or streaming inference].
I’m preparing for Google Summer of Code 2026 and would love to contribute to PyMC along the way. I’ve started going through the documentation and the contributing guide on GitHub. Could someone please suggest a good starting point — maybe some beginner-friendly issues or documentation areas that need help?
Looking forward to learning, contributing, and engaging with this amazing community!
Welcome, Prem! We do have issues tagged as #beginner_friendly, so that’s not a bad place to start. Also, you might want to look at our pymc-examples repo. It consists of the Jupyter notebooks that reside on the PyMC website. Most of its issues are essentially reviews of new or revised notebooks. We value newcomer reviews there particularly because its not always obvious to us when things don’t make sense or are unclearly explained.
Hi everyone!
My name is Jiya Gupta, a pre-final year student at IIT Kharagpur, pursuing a micro-specialization in AI and its Applications.
My interests lie in machine learning and probabilistic modeling. I enjoy working with Python and related ML tools, and I’m keen to learn more about PyMC and contribute to its development.
I’m excited to be part of the PyMC community, learn from experienced contributors, and grow as an open-source contributor.
Looking forward to collaborating and learning from you all!
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