From the course: DJ Patil: Ask Me Anything

What makes up a good data science team?

From the course: DJ Patil: Ask Me Anything

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What makes up a good data science team?

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(upbeat music) - [Interviewer] How do you determine what kind of team you want to build? What are you looking for in your team members? - So, first off, is that they want to be part of a team. Many times, especially some of the training we receive in academia for those that come out of that realm, is that we're used to locking ourselves behind a door and working on the problem in a deep way. And there's some problems that are very well suited for that. But the majority of the problems work best when you're willing to collaborate, you're willing to trade notes, and you're very open about what's working. You're try to go for a high-speed iteration is typically what I like to say is first derivative positive, second derivative positive. You want not only your velocity to be positive, but your acceleration on a problem to be positive. And so you're able to turn and get more out of it. When you're also thinking about that is how do people communicate and share with the data? And how do they have that deep curiosity to say, what if? What about this? Wonder how this? And when that comes together, then you get the more interesting solutions. That's when stuff starts to get more powerful. Now, as to the type of data science team do you need it more decision sciences where people are doing more classic what people refer to as business intelligence? Do you need more of an analytics team that's trying to find insights? Is it more of a fraud and security team, or more of the machine learning, modeling team? Depends on the problem. But I don't think it's a good idea to try to have a one-size-fits-all type of team. You should try to have a team that has both a diversity, deep curiosity, but a passion for focusing on the problems at hand and pushing the ball forward for the mission of the organization. (upbeat music)

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