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EPISODE · Aug 26, 2018 · 27 MIN

Agile Development for Data Scientists, Part 2: Where Modifications Help

from Linear Digressions · host Katie Malone

There's just too much interesting stuff at the intersection of agile software development and data science for us to be able to cover it all in one episode, so this week we're picking up where we left off last time. We'll give a quick overview of agile for those who missed last week or still have some questions, and then cover some of the aspects of agile that don't work well out-of-the-box when applied to data analytics. Fortunately, though, there are some straightforward modifications to agile that make it work really nicely for data analytics! Relevant links: https://www.agilealliance.org/agile101/12-principles-behind-the-agile-manifesto/ https://www.locallyoptimistic.com/post/agile-analytics-p1/ https://www.locallyoptimistic.com/post/agile-analytics-p2/ https://www.locallyoptimistic.com/post/agile-analytics-p3/

Episode metadata supplied by the publisher feed · Published Aug 26, 2018

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Agile Development for Data Scientists, Part 2: Where Modifications Help

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