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Top Analytics/Data Science Tools
the top 10 most popular tools in 2016 poll
| Tool | 2016 % share | % change | % alone |
| R | 49% | +4.5% | 1.4% |
| Python | 45.8% | +51% | 0.1% |
| SQL | 35.5% | +15% | 0% |
| Excel | 33.6% | +47% | 0.2% |
| RapidMiner | 32.6% | +3.5% | 11.7% |
| Hadoop | 22.1% | +20% | 0% |
| Spark | 21.6% | +91% | 0.2% |
| Tableau | 18.5% | +49% | 0.2% |
| KNIME | 18.0% | -10% | 4.4% |
| scikit-learn | 17.2% | +107% | 0% |
In this table 2016 % share is % of voters who used this tool, % change is the change in share vs 2015 poll, and % alone is the percent of voters who used only the reported tool among all voters who used that tool. E.g. 4.4% of KNIME voters reported using only KNIME and nothing else. We note a decrease in such lone voting, with only 9 tools having 5% or more lone votes.
Fig 1: KDnuggets Analytics/Data Science 2016 Software Poll: top 10 most popular tools in 2016
Compared to 2015 KDnuggets Analytics/Data Science Poll results, the only newcomer in top 10 was scikit-learn, displacing SAS.
Tools with the highest growth (among tools with at least 15 users in 2015) were
| Tool | % change | 2016 %share | 2015 %share |
| Dato | 377% | 2.4% | 0.5% |
| Dataiku | 292% | 7.8% | 2.0% |
| MLlib | 253% | 11.6% | 3.3% |
| H2O | 233% | 6.7% | 2.0% |
| Amazon Machine Learning | 171% | 1.9% | 0.7% |
| scikit-learn | 107% | 17.2% | 8.3% |
| IBM Watson | 99% | 4.2% | 2.1% |
| Splunk/ Hunk | 98% | 2.2% | 1.1% |
| Spark | 91% | 21.6% | 11.3% |
| Scala | 79% | 6.2% | 3.5% |
This year, 86% of voters used commercial software and 75% used free software. About 25% used only commercial software, and 13% used only open source/free software. A majority of 61% used both free and commercial software, similar to 64% in 2015.
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