文档介绍：2016 Data Science Salary Survey
Tools, Trends, What Pays (and What Doesn’t) for Data Professionals
John King & Roger analyzed input from 983 respondents working in the data space, across a variety of industries— representing 45 countries and 45 US states. Through the results of our 64-question survey, we’ve explored which tools data scientists, analysts, and engineers use, which tasks they engage in, and of course—how much they make.
Key findings include:
Python and Spark are among the tools that contribute most to salary.
Among those who code, the highest earners are the ones who code the most.
SQL, Excel, R and Python are the most commonly used tools.
Those who attend more meetings, earn more.
Women make less than men, for doing the same thing.
Country and US state GDP serves as a decent proxy for geographic salary variation (not as a direct estimate, but as an additional input for a model).
The most salient division between tool and tasks usage is between those who mostly use Excel, SQL, and a small
number of closed source tools—and those who use more open source tools and spend more time coding.
R is used across this division: even people who don’t code much or use many open source tools, use R.
A secondary division emerges among the coding half— separating a younger, Python-heavy data scientist/analyst group, from a more experienced data scientist/engineer cohort that tends to use a high number of tools and earns the highest salaries.
To see our complete model and input your own metrics to predict salary, see Appendix B (but beware—there’s a trans- formation involved: don’t forget to square the result!).
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Executive Summary
2016 DATA SCIENCE SAL ARY SURVEY
non-US respondents and respondents aged 30 or younger. Three-fifths of the sample came from the US, and these respondents had a median salary of $106K.
Understanding Interquartile Range
For a number of survey questions, we show graphs of answer shares and the median salaries of respondents who ga