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Does it even work?

Ilro Lee

“What does agile data science mean?” you might be asking. In one word: agile! Agile is a methodology that has been embraced by many industries, including data science. It’s time to get agile with your data science projects and start increasing efficiency and decreasing costs. This blog post talks about what agile data science is, how it can help you manage your projects better, and tips around how it can be used in the context of your company’s culture.

What I mean by agile data science is that the agile methodology can be applied to data science projects. For some people, this might not sound like anything exciting, but for some, this could be a game-changer.

The agile manifesto states:

– “deliver working software frequently”

– “customer collaboration over contract negotiation”

– “responding to change over following a plan”.

This is essentially agile data science in practice, what agile data scientists do on the daily basis. They collaborate with their clients and deliver working software frequently…I acknowledge that there is a wide range of project types, expectations, and skill levels of those involved.

This means agile data science focuses on iterative development and delivering working software or solutions frequently. This makes agile projects more like small startups than traditional waterfall projects where the client only sees the end result at the very end of a project, which can take years.

Agile data scientists are also less concerned about upfront requirements gathering because they know that requirements are likely to change. They instead focus on agile data science minimum viable products (MVPs) that are the smallest solution possible for their clients’ needs and then iterate based on feedback from their client, which makes agile data scientists more like product managers than traditional software developers or engineers who focus much more heavily on planning work upfront.

Agile data science project management can be described as a flexible and efficient method for managing data science projects. It has become popular in the last decade as many software programmers have realized that just because they can do something doesn’t mean it should be done. They have learned that an agile methodology is a great tool for managing projects and coming up with high-quality products in short time frames, despite limited resources or strict deadlines.

The agile method also works…

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