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Getting Started in Open Source
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Data Science Renee
Tags: coding / data science / open source / programming
From Rebecca Bilbro:
“The phrase ‘open source’ evokes an egalitarian, welcoming niche were programmers can work together towards a common purpose–creating software to be freely available to the public in a community that sees contribution as its own reward. But for data scientists who are just entering into the open source milieu, it can sometimes feel like an intimidating place….
And yet, open source development does have a lot going for it:
- Users have access to both the functionality and the methodology of the software (as opposed to just the functionality, as with proprietary software).
- Contributors are also users, meaning that contributions track closely with user stories, and are intrinsically (rather than extrinsically) motivated.
- Everyone has equal access to the code, and no one is excluded from making changes (at least locally).
- Contributor identities are open to the extent that a contributor wants to take credit for her work.
- Changes to the code are documented over time.”
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