Top Algorithms and Methods Used by Data Scientists

Regional distribution of poll participants.

• US/Canada, 40%
• Europe, 32%
• Asia, 18%
• Latin America, 5.0%
• Africa/Middle East, 3.4%
• Australia/NZ, 2.2%

As in 2011 poll, we combined Industry/Government in one group and Academic researchers/Students into a second group, and computed the "affinity" of the algorithm to Industry/Gov as

N(Alg,Ind_Gov) / N(Alg,Aca_Stu)
------------------------------- - 1
N(Ind_Gov) / N(Aca_Stu)

Thus algorithm with affinity 0 is used equally in Industry/Government and by Academic Researchers or students. The higher IG affinity the more "industrial" is the algorithms, and the lower it is the more "academic" is the algorithm.

The most "Industrial Algorithms" were:

• Uplift modeling, 2.01
• Anomaly Detection, 1.61
• Survival Analysis, 1.39
• Factor Analysis, 0.83
• Time series/Sequences, 0.69
• Association Rules, 0.5

While the uplift modeling was again the most "industrial algorithm", the surprising finding is that it is used by so few - only 3.1% - the lowest of any algorithm in this poll.

The most academic algorithms were

• Neural networks - regular, -0.35
• Naive Bayes, -0.35
• SVM, -0.24
• Deep Learning, -0.19
• EM, -0.17

Next figure shows all the algorithms and their Industry/Academic affinity.


Fig. 3. KDnuggets Poll: Top Algorithms used by Data Scientists: Industry vs Academia

Next table has the details on the algorithms, % respondents who used them in 2016 and 2011 Poll, change (%2016 / %2011 - 1), and Industry affinity as explained above.

Table 3: KDnuggets 2016 Poll: Algorithms Used by Data Scientists
Next table has the details on the algorithms, with columns

• N: Rank according to share of usage
• Algorithm: algorithm name,
• Type: S - Supervised, U - Unsupervised, M - Meta, Z - Other,
• % respondents who used this algorithm in 2016 Poll
• % respondents who used this algorithm in 2011 Poll
• change (%2016 / %2011 - 1), and
• Industry affinity as explained above.

Table 4: KDnuggets 2016 Poll: Algorithms Used by Data Scientists

N	Algorithm	Type	2016 % used	2011 % used	% Change	Industry Affinity
1	Regression	S	67%	58%	16%	0.21
2	Clustering	U	57%	52%	8.7%	0.05
3	Decision Trees/Rules	S	55%	60%	-7.3%	0.21
4	Visualization	Z	49%	38%	27%	0.44
5	K-nearest neighbors	S	46%			0.32
6	PCA	U	43%			0.02
7	Statistics	Z	43%	48%	-11.0%	1.39
8	Random Forests	S	38%			0.22
9	Time series/Sequence analysis	Z	37%	30%	25.0%	0.69
10	Text Mining	Z	36%	28%	29.8%	0.01
11	Ensemble methods	M	34%	28%	18.9%	-0.17
12	SVM	S	34%	29%	17.6%	-0.24
13	Boosting	M	33%	23%	40%	0.24
14	Neural networks - regular	S	24%	27%	-10.5%	-0.35
15	Optimization	Z	24%			0.07
16	Naive Bayes	S	24%	22%	8.9%	-0.02
17	Bagging	M	22%	20%	8.8%	0.02
18	Anomaly/Deviation detection	Z	20%	16%	19%	1.61
19	Neural networks - Deep Learning	S	19%			-0.35
20	Singular Value Decomposition	U	16%			0.29
21	Association rules	Z	15%	29%	-47%	0.50
22	Graph / Link / Social Network Analysis	Z	15%	14%	8.0%	-0.08
23	Factor Analysis	U	14%	19%	-23.8%	0.14
24	Bayesian networks	S	13%			-0.10
25	Genetic algorithms	Z	8.8%	9.3%	-6.0%	0.83
26	Survival Analysis	Z	7.9%	9.3%	-14.9%	-0.15
27	EM	U	6.6%			-0.19
28	Other methods	Z	4.6%			-0.06
29	Uplift modeling	S	3.1%	4.8%	-36.1%	2.01

Related:

• The 10 Algorithms Machine Learning Engineers Need to Know
• 10 Algorithm Categories for A.I., Big Data, and Data Science
• Why Implement Machine Learning Algorithms From Scratch?