Industry Insights

After 8 years, the race is officially over! Python is the tool of choice for data scientists & analytics professionals now and for the foreseeable future.As many of you probably know, we started this survey in 2014 to ask industry professionals whether they preferred using SAS or R, then adding Python in 2016 as it became more commonly used. Since then, Python has continuously increased in popularity year over year.

Which tool do you prefer to use: SAS, R, or Python?

In our 2021 sample, nearly half (48%) of data scientists and analytics professionals we surveyed preferred using Python, 29% prefer SAS, and 23% prefer R. When compared to our the results of our 2020 survey, the percentage who preferred Python is nearly the same, with R and SAS jockeying for second place.

Looking at our survey results since 2014, you can see just how much preferences have shifted over the past eight years!

Examining Tool Preferences by Demographic Factors

Although in years past we’ve typically segmented the sample by different demographic factors to examine how preferences vary, we’ve decided it’s officially time to put this question to rest until there are new tool options for us to compare in the data science & analytics field that challenge the mainstream incumbents.However, if you’re still interested in seeing how preferences tend to vary by years’ experience, industry, degree, and other factors, you can check out our 2020 blog with more detailed analysis. A few trends that we’ve observed over the years include:

1. Support for Python is highest among students and professionals with 0-5 years of work experience.
2. Professionals whose areas of study were Engineering, Computer Science, or Natural Sciences tend to prefer Python, while those who studied Social Sciences tend to prefer R.
3. While some more traditional teams in industries like Financial Services or Pharmaceuticals may still be using SAS, we continue to see more of these teams converting to Python or allowing professionals to choose their tools.

Survey Respondent Comments

Each year we’ve also asked respondents to share their thoughts on tools with us, so that we could include some of their comments!"Python! SAS is too cumbersome, and R is limited for production purposes." "Prefer R, but use Python most of the time because of work requirements" "I prefer Python, sometimes I still use R, and I only used SAS when I worked for a very large corporation. "R but my current team is monolingual and so I've had to use Python more often. Despite that, I still use R more regularly." "Prefer SAS, but have completely migrated to Python" "I use both SAS and Python. I prefer SAS, but Python has some benefits in a production environment that can’t be denied – so both are required for my work." "Broadly speaking, we have moved from SAS to R to Python. Sometimes we still use R, but rarely SAS. Still have some affection for the company, though"

Closing Thoughts

Our survey examines tool preferences, and we continue to see industry trends emerge from our network of professionals. Some may prefer one tool, but primarily use a different tool at their place of work because that’s what their company/team demands. This scenario can be a possible early indicator of future attrition if the company doesn’t move to the “latest and greatest”, as many professionals value keeping up with evolving technology as an important career objective.We continuously stand behind the idea that, to have the most options in the job market (both for job seekers and employers), it’s beneficial to pay attention to technology trends and preferences. This continuous learning allows data science & analytics professionals and leaders to constantly evolve with the industry to stay competitive amongst their peers.Thank you to everyone in our network who participates in our numerous surveys! Your insights are invaluable for the community and have allowed us to examine many important market trends over the years. You can view more of our past surveys here, and watch this space for more results in the coming weeks.