From the course: Learning Data Science: Using Agile Methodology

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See how to use a DSLC

See how to use a DSLC

From the course: Learning Data Science: Using Agile Methodology

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See how to use a DSLC

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- Data science doesn't work very well with existing process life cycles. It's not enough like software to fit into the SDLC. The CRISP-DM data mining process is a little too rigid for quick results. That doesn't mean that a data science team should work in whatever way feels right. There's some real value in these life cycles. One value is that they give you a high level map of where you're going. This is useful when you're just starting a data science team. You can get a clear sense of the path forward. That way, you'll start with the end in mind. The danger in these life cycles is that it becomes a primary focus of your work. You want to use the life cycles as a way to do better data science. You don't want to follow the process for the sake of following the process. A good life cycle should be like a hand rail. It's good to know it's there, but you don't want to cling to it with every step. After a while, you shouldn't even notice that it's there. For data science projects, you can…

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