Introduction

FOR THE THIRD YEAR RUNNING, we at O’Reilly Media have collected survey data from data scientists, engineers, and others in the data space about their skills, tools, and salary. Some of the same patterns we saw last year are still present—newer, scalable open source tools in general correlate with higher salaries, Spark in particular continues to establish itself as a top tool. Much of this is apparent from other sources: large software companies that traditionally produced only proprietary software have begun to embrace open source; Spark courses, training programs, and conference talks have sprung up in great numbers. But who actually uses which tools (and are the old ones really disappearing)? Which tools do the highest earners use, and is it fair to attribute a particular variation in salary to using a certain tool? We hope that the findings in this iteration of the Data Science Salary Survey will go beyond what is already obvious to any data scientist or Strata attendee.

Preliminaries

This report is based on an online survey open from November 2014 to July 2015, publicized to the O’Reilly audience but open to anyone who had the link. Of the 820 respondents who answered at least one question, about a quarter dropped out before completing the survey and have been excluded from all segments of analysis except for those showing responses to single questions. We should be careful when making conclusions about survey data from a self-selecting sample—it is a major assumption ...

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