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Data-intensive research workflows in Julia

Lesson

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.

Contributing

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.

We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.

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 good_first_issue. This indicates that the maintainers will welcome a pull request fixing this issue.

Maintainer(s)

Current maintainers of this lesson are

Authors

A list of contributors to the lesson can be found in AUTHORS

Citation

To cite this lesson, please consult with CITATION