Industry Insights

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SAS, R, or Python Survey 2016: Which Tool Do Analytics Pros Prefer?

July 13, 2016
The results of our 2019 SAS, R, or Python survey have been released! Click here to see our latest analysis.

For many quantitative business professionals, the debate around analytics tools has been known to rival the enthusiasm of political conversations at Thanksgiving dinner. Over the past two years we’ve sent out an immensely popular SAS vs. R survey to our network of quantitative professionals, generating over 1,000 (often very passionate) responses each year. But, last year, we received enough write-in requests to include Python in our survey that we decided to evolve with the times!To keep things simple, we only asked one question – Which do you prefer to use: SAS, R, or Python?

Overall Donut
Over time

Our results show that support for open source tools has been steadily climbing over the past three years, with 61.3% of respondents this year choosing R or Python, and 38.6% choosing SAS. As per usual, there were a few professionals who responded with neither/both, or with write-ins for other tools. Who knows, perhaps our survey will have to evolve again next year? We’ll see!Eager to learn more about these numbers? So were we! After tallying the initial results we couldn’t wait to dig into our survey data from the past three years and see what we could discover.

Region 2016

In this year’s results, it’s easy to see that the largest proportion of Python supporters are on the coasts, with the West Coast edging more towards open source options R and Python than any other region. R support is highest in the Mountain region, and SAS support continues to be highest in the Midwest and Southeast.

Industry 2016

Similar to last year, Tech/Telecom most heavily favors open source options R and Python, which is not too surprising given the prevalence of West Coast supporters. As in 2014 and 2015, SAS continues to have a stronghold in the Retail/CPG and Financial Services industries.

Education 2016

Professionals with a Ph.D. are more likely to prefer R than SAS, while Bachelor’s holders are more likely to prefer SAS than R. Support for Python varies slightly, but is strongest amongst professionals with a Ph.D.

Years Experience 2016

Support for open source tools R and Python is the greatest amongst professionals with 0-5 years’ experience, while support for SAS is greatest amongst professionals with 16+ years’ experience. As more universities have moved towards teaching open source tools like R and Python in their programs, we’ve seen more support for these options from junior professionals.

DS vs PAP donut

Python holds the majority of support amongst data scientists, with SAS only holding 3% amongst this group. This is likely due to its limitations when building the custom tools that many data scientists use to manage unstructured data. Amongst other predictive analytics professionals, SAS and R support is relatively even. For more information about how we define "data scientist", check out this blog.


Here you can see how tool preferences changed for data scientists and other predictive analytics professionals since our 2015 survey.A Few Lively CommentsWith so many participants in our flash surveys, we always receive quite a few entertaining observations that we enjoy sharing with you. Here are just some of the enjoyable responses we collected:

  • SAS 😊😊😊 All the way
  • R is a nightmare.
  • I've turned my team into a Python shop last year; we have no regrets
  • R. Then Python. I never use SAS anymore :-)
  • SAS is so intuitive to use for data analysis
  • SAS should be very concerned, as if they are a red giant transitioning to a white dwarf.
  • SAS, R, *and* Python!
  • I know 7 dozen PhDs in Germany that would say Python
  • SAS ..... probably because I'm over 50. ; )
  • R - Anytime, Anywhere, Anything
  • Python is the future
  • 1st SAS, 2nd R, 999th Python :)

If you’re interested in comparing any of these results to our “deeper dive” from last year, you can check out this blog post. We also presented more detail on these results in a 15-minute webinar, and you can find the recording on our YouTube channel. We always find the evolution of tools and methodologies in predictive analytics to be pretty interesting, so we look forward to this every year. What did you think of the results, did anything surprise you? Let us know in the comments!

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