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Data Scientists: Skills Mix, Team Makeup

We have published many articles on this subject, for instance:

(click here for more such articles)

But this one is an external study published in BusinessOverBroadway, with several great charts, and also focusing on team makeup.

Here's the introduction:

A new survey of 490 data professionals from small to large companies, conducted by AnalyticsWeek in partnership with Business Over Broadway, provides a look into the field of data science. Download the free Executive Summary of the report, Optimizing your Data Science Teams

Our world of Big Data requires that businesses, to outpace their competitors, optimize the use of their data. Understanding data is about extracting insights form the data to answer questions that will help executives drive their business forward. Do we invest in products or services to improve customer loyalty? Would we get greater ROI by hiring more staff or invest in new equipment?

Getting insights from data is no simple task, often requiring data science experts with a variety of different skills. Many pundits have offered their take on what it takes to be a successful data scientist. Required skills include expertise in business, technology and statistics. In an interesting study published by O'Reilly, researchers (Harlan D. HarrisSean Patrick Murphy and Marck Vaisman) surveyed several hundred practitioners, asking them about their proficiency in 22 different data skills. Confirming the pundits' list of skills, these researchers found that data skills fell into five broad areas: Business, ML / Big Data, Math / OR, Programming and Statistics.

Two great charts from the article:

1. Proficiency by Job Role

2. Proficiency (not broken down by job role)

Click here to read the full article, with numerous charts, details about the survey, and conclusions.

Would you add any other skills? These are mostly technical; I'd add domain expertise, business acumen, ability to present and convince, visualization, planning etc. though these are not specific to data science.. 

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Comment by ®γσ, Lian Hu ENG on October 8, 2015 at 7:39am
Thats why journey to be data scientist is not easy... Keep up training and learning

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