Because they are not yet employed in the field, these students are excluded from the other samples (overall results, industry, region, etc.). For those who have already graduated, they were counted as students if they are still in internships, completing post-graduate work, or are newly-graduated and not yet employed in a data science or analytics position. *College/graduate students shows the preferences of Bachelor’s, Master’s, and PhD students, the majority of whom are (or will be) in the graduating class of 2018-2020.
Since we’ve noticed early career professionals increasingly gravitating towards open source tools like R, and especially Python in the past few years, this year we thought it would be interesting to ask the data science and analytics students in our network to see how their preferences compared to working professionals. other predictive analytics professionals. SAS, R, or Python Preferences Examined by Demographic FactorsĮach year we also combine participant responses with demographic information to show how respondent preferences vary by factors like region, industry, years of experience, education, and data scientists vs. R squeaked by SAS to narrowly claim second place with 30% of the vote over SAS’ 29%. With the stakes for victory running high, responses were fiery this year with over 1,000 votes counted.įor comparison purposes, we only ask one question: Which do you prefer to use – SAS, R, or Python?Īs you can see from the six year trend, Python has more than doubled its share of the vote since its introduction in 2016 – from 20% to 41%. What began as a “spirited” debate between SAS and R users in 2014 and 2015, became even more animated when Python was added to the mix in 20, culminating in a dead heat between all three competitors in our 2018 survey results. 2019 SAS, R, or Python Survey Update: Which Tool do Data Scientists & Analytics Pros Prefer?Įach year for the past six years, we’ve asked data scientists and analytics professionals whether they prefer to use SAS, R, or Python and examined their responses to see how tool preferences vary by a variety of factors.
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