2024-12-07 –, Main Stream
Language: English
First experience of stepping into the rabbit hole of contributing to open-source software, highlighting key learnings and practical steps for beginners. It covers overcoming self-doubt, learning through collaboration, and the unexpected joys of community engagement. What you can learn from contributing to Open Source and what you probably will not as an aspiring Data Scientist.
Description:
In this talk I want to share my journey from a teacher and street-artist to a Data Science autodidact.
On my way I met PyLadies and Open Source, and just like Alice in Wonderland I was baffled by how exciting but also challenging OSS can be. In my talk I would like to share how to embrace uncertainty, how to gain self-confidence. Last but not least I would like to share practical tips on how to start contributing to Open Source.
0-5: Short story of a self-learner that wants to transition into Data Science/Machine Learning
5-10: How and why I started to contribute to OSS and the challenges I’m facing and what “underwater unicorn magic” has to do with it.
10-15: What I’ve learned thanks to OSS, what I didn’t
Prior Knowledge Expected & Audience: This talk is aimed at aspiring contributors, autodidacts, students and anyone curious about the OSS ecosystem. It can be interesting both for Data Scientists as software engineers, but no prior knowledge is expected
Format & Tone: Light-hearted session filled with anecdotes, practical tips, and a dash of humor.
Key Learnings: Participants will gain:
-A clear starting point for open-source contribution.
-Tips to manage initial doubts and hesitation
-Insights into the value of community and mentorship in tech.
-Encouragement to contribute
Data and machine learning enthusiast with a soft spot for open-source software.
Driven by curiosity, eager self-learner, Kaggle Notebook Expert, Datathons enjoyer, PyData volunteer, currently engaged in Women in AI Mentorship, exploring and contributing to Open Source Land and building my Machine Learning portfolio.