In any technical development field, having other practitioners review your work before shipping code off to production is a valuable support tool to make sure your work is error-proof. Even through your preparation for the review, improvements might be discovered and then other issues that escaped your awareness can be spotted by outsiders. This peer scrutiny can also be applied to Data Science, and this article outlines a process that you can experiment with in your team.
Originally from KDnuggets https://ift.tt/34vVlNj
source https://365datascience.weebly.com/the-best-data-science-blog-2020/peer-reviewing-data-science-projects
Published by 365Data Science
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