ere are the results, based on 844 voters.

The top 10 algorithms and their share of voters are:


Fig. 1: Top 10 algorithms used by Data Scientists.
See full table of all algorithms at the end of the post.

The average respondent used 8.1 algorithms, a big increase vs a similar poll in 2011.

Comparing with 2011 Poll Algorithms for data analysis / data miningwe note that the top methods are still Regression, Clustering, Decision Trees/Rules, and Visualization. The biggest relative increases, measured by (pct2016 /pct2011 - 1) are for

• Boosting, up 40% to 32.8% share in 2016 from 23.5% share in 2011
• Text Mining, up 30% to 35.9% from 27.7%
• Visualization, up 27% to 48.7% from 38.3%
• Time series/Sequence analysis, up 25% to 37.0% from 29.6%
• Anomaly/Deviation detection, up 19% to 19.5% from 16.4%
• Ensemble methods, up 19% to 33.6% from 28.3%
• SVM, up 18% to 33.6% from 28.6%
• Regression, up 16% to 67.1% from 57.9%
  

Most popular among new options added in 2016 are

• K-nearest neighbors, 46% share
• PCA, 43%
• Random Forests, 38%
• Optimization, 24%
• Neural networks - Deep Learning, 19%
• Singular Value Decomposition, 16%
  

The biggest declines are for

• Association rules, down 47% to 15.3% from 28.6%
• Uplift modeling, down 36% to 3.1% from 4.8% (that is a surprise, given strong results published)
• Factor Analysis, down 24% to 14.2% from 18.6%
• Survival Analysis, down 15% to 7.9% from 9.3%
  

The following table shows usage of different algorithms types: Supervised, Unsupervised, Meta, and other by Employment type. We excluded NA (4.5%) and Other (3%) employment types.

Table 1: Algorithm usage by Employment Type

Employment Type	% Voters	Avg Num Algorithms Used	% Used Super- vised	% Used Unsuper- vised	% Used Meta	% Used Other Methods
Industry	59%	8.4	94%	81%	55%	83%
Government/Non-profit	4.1%	9.5	91%	89%	49%	89%
Student	16%	8.1	94%	76%	47%	77%
Academia	12%	7.2	95%	81%	44%	77%
All		8.3	94%	82%	48%	81%

We note that almost everyone uses supervised learning algorithms.
Government and Industry Data Scientists used more different types of algorithms than students or academic researchers,
and Industry Data Scientists were more likely to use Meta-algorithms.

Next, we analyzed the usage of top 10 algorithms + Deep Learning by employment type.

Table 2: Top 10 Algorithms + Deep Learning usage by Employment Type

Algorithm	Industry	Government/Non-profit	Academia	Student	All
Regression	71%	63%	51%	64%	67%
Clustering	58%	63%	51%	58%	57%
Decision	59%	63%	38%	57%	55%
Visualization	55%	71%	28%	47%	49%
K-NN	46%	54%	48%	47%	46%
PCA	43%	57%	48%	40%	43%
Statistics	47%	49%	37%	36%	43%
Random Forests	40%	40%	29%	36%	38%
Time series	42%	54%	26%	24%	37%
Text Mining	36%	40%	33%	38%	36%
Deep Learning	18%	9%	24%	19%	19%

To make the differences easier to see, we compute the algorithm bias for a particular employment type relative to average algorithm usage as Bias(Alg,Type)=Usage(Alg,Type)/Usage(Alg,All) - 1.


Fig. 2: Algorithm usage bias by Employment.