Julia, 316% growth, from 0.7% share in 2013 to 2.9% in 2014

SAS, 76% growth, from 20.8% in 2013 to 36.4% in 2014

Scala, 74% growth, from 2.2% in 2013 to 3.9% in 2014

F#, 100% decline, from 1.7% share in 2013 to zero in 2014

C++/C, 60% decline, from 9.3% in 2013 to 3.6% in 2014

GNU Octave, 57% decline, from 5.6% in 2013 to 2.4% in 2014

MATLAB, 50% decline, from 12.5% in 2013 to 6.3% in 2014

Ruby, 44% decline, from 2.2% in 2013 to 1.3% in 2014

Perl, 41% decline, from 4.5% in 2013 to 2.6% in 2014

What programming/statistics languages you used for an analytics / data mining / data science work in 2014?

Language used % voters in 2014 (719 total)

% voters in 2013 (713 total)

% voters in 2012 (579 total) R (352 voters in 2014) 49.0%

60.9%

52.5% SAS (262) 36.4%

20.8%

19.7% Python (252) 35.0%

38.8%

36.1% SQL (220) 30.6%

36.6%

32.1% Java (89) 12.4%

16.5%

21.2% Unix shell/awk/sed (63) 8.8%

11.1%

14.7% Pig Latin/ Hive/ other Hadoop-based languages (61) 8.5%

8.0%

6.7% SPSS (58) 8.1%

not asked

not asked MATLAB (45) 6.3%

12.5%

13.1% Scala (28) 3.9%

2.2%

2.4% C/C++ (26) 3.6%

9.3%

14.3% Julia (21) 2.9%

0.7%

0.3% Other low-level languages (20) 2.8%

5.9%

11.4% Perl (19) 2.6%

4.5%

9.0% GNU Octave (17) 2.4%

5.6%

5.9% Ruby (9) 1.3%

2.2%

3.8% Lisp/Clojure (5) 0.7%

1.0%

4.3% F# (0) 0%

1.7%

not asked in 2012

US/Canada, 51.6%,

Europe: 26.7%,

Asia: 13.3%,

Latin America: 3.7%,

Africa/Middle East: 3.5%

AU/NZ: 2.0%

Sometimes the high-level data science platform is not enough for a particular analytics task, and data scientists need to go to a lower level statistics / programming language.The last KDnuggets poll askedThe results show that the main 4 languages - R, Python, SAS, and SQL - hold a commanding lead - 91% of all respondents used one of them.Comparing with similar KDnuggets Pollsin 2013: What programming/statistics languages you used for analytics / data mining , and in 2012 , we note several changes and trends.perhaps partly driven by growth and change in KDnuggets readers composition, and likely also by increased visibility of this poll among SAS users. SAS users had a high percentage of "lone" votes - in 2014, 58% of them said they used only SAS, compared to 26% in 2013. The fraction of "lone" votes in 2014 was 20.5% for R, 14% for Python, and only 4.5% for SQL.- R, SAS, Python, and SQL. 91% of all voters have used at least one of them. Almost all other languages declined in their popularity for data mining tasks, including Java, Unix shell, MATLAB, C/C++, Perl, Octave, Ruby, Lisp, and F#.Here is a Venn diagram that shows significant overlap between R, Python, and SQL. The percentages indicated how many voters chose that option, eg 20% of all voters have used both R and Python, while 10% have used R, Python, and SQL. The areas of the circles and intersections approximately correspond to the fraction of voters.Here is a similar Venn diagram showing overlap between R, Python, and SAS. We see that SAS is much more independent from R and Python, with about 2/3 of of SAS users not using R or Python.in share of usage wereHere is the table with more details:Among other programming languages William Dwinnell mentioned Compiled BASIC (PowerBASIC).Regional participation wasThis is similar to 2013, but with more participation from Asia and Africa/Middle East (led by Israel and Turkey), and less from Latin America (main decline from Brazil, perhaps still depressed from the World Cup loss).