For many quantitative business professionals, the argument around analytics tools is known to rival the enthusiasm of political conversations around the dinner table at yearly family get togethers. Over the past two years over 1,000 (often very passionate) responses were returned regarding an SAS vs. R survey from quantitative professionals. But, last year, there were enough requests to include Python.
To keep things simple, one question was asked – Which do you prefer to use: SAS, R, or Python?
The results show that use of open source tools is climbing over the past three years, with 61.3% of respondents in 2016 choosing R or Python, and 38.6% choosing SAS. There were a few professionals who chose neither/both, or wrote-in for other tools. Perhaps the survey will have to evolve again next year to include other languages.
Are you eager to learn more about the poll numbers? Likewise! After tallying the initial results the survey data was further analyzed from the past three years to see what could be discovered.
Similar to last year, Tech/Telecom most heavily favors R and Python, which is not surprising given the prevalence of the West Coast supporters. As in 2014 and 2015, SAS continues to have a strong presence in the Retail/CPG as well as Financial Services industries.
Data Scientists with a Ph.D. are more likely to use R than SAS, while Bachelor’s holders prefer SAS over R. Support for Python varies, but is strongest amongst Data professionals with a Ph.D.
Support for R and Python is greatest amongst professionals with 0-5 years’ experience, and support for SAS is greatest amongst data professionals with 16+ years’ experience. With more universities moving towards teaching tools such as R and Python, the support for these softwares from junior professionals has increased.
Python has he majority of support from data scientists, with SAS only holding 3% among this group. It is likely due to the limitations when building custom tools that many data scientists are using to manage unstructured data. Amongst other analytics professionals, the support for SAS and R is even.
Here is data from a similar set of data from respondents in 2015 to see how preferences for tools have changed in the past year.