2024-09-28 –, 4F Track4
The open source community is all about giving back and learning from one another. No matter how small, every contribution is valuable. And everyone can contribute something with a little bit of help. The hardest part is finding something to work on that fits your interests and skills.
In this talk, I will provide five ways that I used to get started contributing to different open source projects. I also share some guidance on selecting projects to contribute to and how to set yourself up for success. Get ready to start your open source journey!
I have contributed to several Python open source projects (e.g., NumPy, pandas, Matplotlib, Scikit-learn, numpydoc) using a variety of strategies to find my entry point. Many people have asked me how they can do the same, so I created this talk to spread that knowledge.
Knowledges and know-how the audience can get from your talk –- Learn how people of all levels can contribute to open source projects.
- Learn how you can contribute to open source projects.
- Learn how to find a project to work on.
- Learn how to find a particular task to work on within a project.
- Learn about sprints, contributing to documentation, navigating an issue tracker, the process for identifying and fixing a bug, and the process for implementing a new feature, using examples from the author's personal experiences contributing to various Python open source projects.
- Learn tips for making a successful contribution.
Audience members should have an interest in contributing to open source, and ideally, should have used at least one open source library before.
Audience experiment –Beginner
Language of presentation –English
Language of presentation material –English
Stefanie Molin is a software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization,” which is currently in its second edition and has been translated into Korean and Chinese. She holds a bachelor’s of science degree in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.