Learn how to use Julia to enable your data-intensive scientific research.
It can be a challenge to know where to start when developing a scalable and reproducible workflow for your data-intensive computations. The Julia programming language is notable for enabling researchers and analysts in diverse domains to get a handle on this challenge. Use this lesson to learn how to start implementing effective scientific computing workflows using Julia.
We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.
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Please see the current list of issues for ideas for contributing to this
repository. For making your contribution, we use the GitHub flow, which is
nicely explained in the chapter Contributing to a Project in Pro Git
by Scott Chacon.
Look for the tag 
Current maintainers of this lesson are
- James Foster: (@jd-foster)
A list of contributors to the lesson can be found in AUTHORS
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