September 2016
Beginner
40 pages
46m
English
Content preview from 2016 Data Science Salary Survey
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The Impact of Tool Choice
The Top Tools
The top two tools in the sample were Excel and SQL, both with use by 69% of the sample, followed by R (57%) and Python (54%). Compared to last year, Excel is up (from 59%), as is R (from 52%), while SQL and Python are only slightly higher than last year.
Over 90% of the sample reported spending at least some time coding, and 80% used at least one of Python, R, and Java, although only 8% used all three. The most commonly used tools (except for operating systems) were included in the model training data as individual coefficients; of these, Python, JavaScript, and Excel had significant coefficients: +4.6, –2.2 and –7.4, respectively. Less commonly used tools were first grouped together into clusters and aggregate features were included that represent counts of tools used from each cluster. For five clusters that were found to have a significant correlation with salary, coefficients are added on a per-tool basis.1
The cluster with the largest coefficient was centered on Spark and Unix, contributing +3.9 per tool. Spark usage was 20%, up from last year’s a modest 3%, and it continues to be used by the more well paid individuals in the sample.
In contrast to the largely open source Spark/Unix cluster, the second highest cluster coefficient (+2.4) was assigned to a cluster dominated by proprietary software: Tableau, Teradata, Netezza, Microstrategy, Aster Data, and Jaspersoft. In last year’s report, Teradata also featured as a tool with a large, ...
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