Titlebook: Data Science; Create Teams That As Doug Rose Book 2016 Doug Rose 2016 data science.team.agile.analytics.data-driven organization.data minin

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Studies in Computational Intelligencetistics and math to see if they can get at answers. Statistics is a very interesting field. To participate in a data science team, you need some basic understanding of the language. There are several terms you need to be familiar with as you explore statistical analysis. They are:

constellation

cancellous-bone

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Springer Proceedings in Complexityves. Many organizations focus on objectives and create powerful compliance departments. These departments ensure that everyone meets those objectives. This focus can keep your team from exploring and discovering. A data science team needs to take advantage of serendipity and add to organizational knowledge.

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Spanning Edge Betweenness in PracticeWe defined data science in Chapter 2 and covered what it means to be a “data scientist.” In this chapter, you’ll see how to break that role into several team roles. Then you’ll see how this team can work together to build a greater data science mindset.

MOAT

https://doi.org/10.1007/978-3-319-54241-6In this chapter, we cover the two of the main pitfalls that affect data science teams. First, if a team reaches a consensus too quickly, it stifles discovery and is a sign that the team has blind spots and is prone to groupthink.

Matrimony

https://doi.org/10.1007/978-3-030-14459-3Most of the people on your data science team will be familiar with a typical project life cycle. People from a software development background are familiar with the software development life cycle (SDLC). People from data mining probably used the Cross Industry Standard Process for Data Mining (CRISP-DM).

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