IN THIS FOURTH EDITION of the O’Reilly Data Science Salary Survey, the input was analyzed from 983 respondents working in the data space, across a variety of industries— representing 45 countries and 45 US states. Through the results of a 64-question survey, the survey explores 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.
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