Latest KDnuggets Poll asked

Which methods/algorithms you used in the past 12 months for an actual Data Science-related application?

.

Here 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 mining

we 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.

We note that Industry/Government are more likely to use Visualization and Time Series,

Next, we look at regional participation which was representative of overall KDnuggets visitors.