February 2017
Beginner
30 pages
24m
English
Content preview from 2017 European Data Science Salary Survey
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Tools
THE TOP FOUR TOOLS FROM EUROPEAN RESPONDENTS WERE EXCEL, SQL, R, AND PYTHON, each used by over half of all respondents. These four tools have kept their top positions in every Data Salary Survey we have conducted, and there does not appear to be any sign of this changing. Almost every respondent reported using at least one, and about half the sample used three or all four.
Commonly used tools with above-average salaries include Scikit-learn (whose users have a median salary of €52K), Spark (€55K), Hive (€57K), and Scala (€70K). Readers may notice that most tools have a higher median salary than the sample-wide median salary of €48K. This is because respondents who use lots of tools tend to earn more (and they are counted in a large number of tool salary medians). The 43% of respondents who used no more than 10 tools had a median salary of €43K, while those who used more than 10 tools had a median salary of €53K.
Since there is significant overlap between users of individual tools, it is useful to consider mutually exclusive groups of respondents based on tool usage. The groups we will define here are based on a simple set of rules, but using a clustering algorithm would produce very similar results. The rules are:
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If someone used Spark or Hadoop, we call them “Hadoop”
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If someone (not in the Hadoop group) uses R and/or Python, they are labeled “R+Python,” “R-only,” or “Python-only,,” as appropriate
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Everyone who uses SQL and/or Excel (usually both), we call “SQL/Excel” ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.