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A beginner's guide to building a data science team Posted on Jul 16 - 2017

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So, you want to build a data science team? Here's some stuff to think about.

Before long, just like this stock photo, you'll have a team of weird orange people with big bulbous heads, who can sit around a table looking at an enormous hologram of a simple bar chart.

In this article we will cover:

  • Definitions of data science
  • The purpose of data science
  • How data science teams should integrate into the organisation
  • Recruiting for data science
  • Team roles
  • Though Econsultancy is marketing focused, there's plenty in here to appeal more broadly.

First, an attempt at a definition

It seems trite to say that data science's applications are broad, but they are. And data science teams come in different forms, within different organisational structures and under different names.

There's a pretty good Venn diagram developed by Drew Conway which gets to the heart of the ambiguous phrase 'data science'.

In Conway's words, "The difficulty in defining these skills is that the split between substance and methodology is ambiguous, and as such it is unclear how to distinguish among hackers, statisticians, subject matter experts, their overlaps and where data science fits."

I recommend heading over to Conway's article to read more of his thoughts. But the basic takeaway for a layman like me is – there's a hell of a lot to learn and many different skillsets that can be brought to bear on data.

Whilst data science has many grey edges, it's probably worth including some fairly dry definitions of two common teams – 'Big data analytics' teams and 'data product' teams. The former looks for predictive patterns in data without necessarily having a preconceived notion of what they are looking for, and the latter works to implement automated systems that are data-driven.

Data products - Ben Chamberlain, senior data scientist at ASOS, describes a data product as "an automated system that generates derived information about our customers such as predicting their lifetime value. This information is then used to automatically take actions like sending marketing messages or it gets sent to another team who use it for insight." View More

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