Learn more about some of the other SAS vs. R vs. Python trends we’ve seen develop over the past four years by watching our 10 minute mini-webinar on YouTube!
As many of you probably know, over the past few years we’ve been gauging statistical tool preferences among data scientists and predictive analytics professionals by sending out a flash survey to our network: the first two years weighing SAS vs. R, and then adding Python to the mix last year as their libraries expanded.
Now, as one might imagine, the discussion is always rather spirited in nature – we’ve had over 1,000 responses every year – and reading the comments has become one of our favorite parts of doing the survey, so feel free to chime in!
To keep the comparison simple, we only asked one question: Which do you prefer to use – SAS, R, or Python?
Over the past four years we’ve seen preference for open source tools steadily climbing, with 66% of respondents choosing R or Python this year. Python climbed from 20% in 2016 to 26% this year.
Each year we also match responses to demographic information, to show how these preferences break down by factors like region, industry, education, years of experience, and more.
Similar to last year, the largest proportion of Python supporters are on the West Coast and in the Northeast, however, the Mountain region is close behind the Northeast, and all regions saw at least some increase in Python preference. R preference is highest in the Midwest and SAS preference is highest in the Southeast.
Open source preference remains high in Tech/Telecom and preference for SAS continues to be higher in more regulated industries like Financial Services and Pharmaceuticals.
Professionals with a Ph.D. are the most likely to prefer open source tools, likely due to the prevalence of R and Python usage in research and academic programs, and the foundation of experience it establishes as they move into business.
Preference of open source tools is by far the highest amongst professionals with 5 or less years’ experience. Even as the specific proportions have changed, this trend has remained fairly constant over the years that we’ve done the survey.
Here you can see the comparison in tool preference between traditional predictive analytics professionals working with structured data versus data scientists working with unstructured or streaming data. There is a clear preference for Python among data scientists, whereas predictive analytics professionals’ preferences were more mixed.
This distinction is interesting when compared to the tool preferences of professionals in the Tech industry vs. the Marketing/Advertising Services and Financial Services industries. As we’ve found in our salary studies for Data Science and Predictive Analytics, 41% of data scientists work in Technology, whereas in predictive analytics 52% of professionals work in Marketing/Advertising Services or Financial Services – only 12% work in Technology.
Support for Python grew slightly among predictive analytics professionals, from 16% in 2016 to 20% this year, however among data scientists the increase was much higher – from 53% in 2016 to 69% in 2017.
A Few Lively Comments
We’ve always encouraged respondents to share their opinions so that we can post some of them here, and yet again they came through! Here are just some that we collected:
R – I am a reformed SAS user.
My vote is for SAS. It’s a great end to end tool hands down!
I’m going to have to go with Python, though I’m a recent migrant from R
R is still going strong – SAS is losing steam and Python is just around the corner
I actually use c++, so I guess none??
SAS, but I’m over 40. Those young kids and their cool sounding software…
First choice is Python, second R and thank god we fired SAS. 😉
I’m a SASsy guy!
Python by a billion miles
If you’d like to see the full results from last year, check out our 2016 SAS, R, or Python blog post, or click here to see more flash surveys, including hiring trends and what sorts of perks analytics professionals receive at work.
Want to learn more? Watch our 10-minute video below, where we go deeper into additional trends that we found in this year’s data.