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In data scientist survey, R is the most-used tool (other than databases)

O'Reilly has just published the results of the Data Scientist Salary Survey, based on data collected from attendees of the O'Reilly Strata conferences in 2012 and 2013. There were some interesting results from the salary portion of the survey: data scientists at early-stage startups earned a median salary of US$130,000 data scientists at public companies earned a higher median salary (US$110,000) than those at private companies (US$100,000) data scientist using primarily open-source tools earned a higher median salary (US$130,000) than those using proprietary tools (US$90,000) On that last point, the tool usage section of the survey also held interesting results. Each respondent listed multiple tools that they used both in data roles and non-data roles, and the results are summarized below: That SQL tops the list is no surprise: most data scientists need to access a database at some point. But of non-database tools, R is the most-used tool, closely followed by Python. From the survey report: The preponderance of R and Python usage is more surprising —operating systems aside, these were the two most commonly used individual tools, even above Excel, which for years has been the go-to option for spreadsheets and surface-level analysis. R and Python are likely popular because they are easily accessible and effective open source tools for analysis.  It's also interesting to note that the "traditional" proprietary data analysis tools, SAS and SPSS, fall at the bottom of the list. This isn't a random sample by any means — the attendees at Strata are heavily weighted towards US-based startups — but it's certainly indicative of where the market for data analysis products is going. R is also the top-ranked data analysis tool in recent surveys by KDNuggets and Rexer Analytics. You can download the full report (free registration required) fom the the O'Reilly website at the link below. O'Reilly Media: 2013 Data Science Salary Survey

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David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid