By Gregory Piatetsky, KDnuggets.

Which Data Science / Machine Learning methods and tools you used in the past 12 months for a real-world application?

Bayesian methods , 49% up, from 11.7% share in 2016 to 17.5% share in 2017

, 49% up, from 11.7% share in 2016 to 17.5% share in 2017 Random Forests® , 32% up, from 35.1% to 46.2%

, 32% up, from 35.1% to 46.2% Deep Learning , 20% up, from 17.2% to 20.6%

, 20% up, from 17.2% to 20.6% Survival Analysis , 13.5% up, from 7.5% to 8.5%

, 13.5% up, from 7.5% to 8.5% Visualization, 9% up, from 46.7% to 51.0%

Gradient Boosted Machines , 20.4%

, 20.4% Conv Nets , 15.8%

, 15.8% Recurrent Neural Networks (RNN) , 10.5%

, 10.5% Hidden Markov Models (HMM) , 4.6%

, 4.6% Reinforcement Learning , 4.2%

, 4.2% Markov Logic Networks , 2.5%

, 2.5% Generative Adversarial Networks (GAN), 2.3%

Singular Value Decomposition (SVD) , 48% down, from 15.4% share in 2017 to 8.1% share in 2016

, 48% down, from 15.4% share in 2017 to 8.1% share in 2016 Graph / Link / Social Network Analysis , 42% down, from 14.0% to 8.1%

, 42% down, from 14.0% to 8.1% Genetic algorithms/Evolutionary methods , 42% down, from 8.3% to 4.8%

, 42% down, from 8.3% to 4.8% EM , 36% down, from 6.4% to 4.1%

, 36% down, from 6.4% to 4.1% Optimization , 26% down, from 23.2% to 17.2%

, 26% down, from 23.2% to 17.2% Boosting , 20% down, from 30.6% to 24.6%

, 20% down, from 30.6% to 24.6% PCA, 14% down, from 40.5% to 34.7%

Affiliation

Industry/Self-Employed, 63%, 8.3 avg. tools used

Student, 15%, 5.7 avg. tools used

Researcher/Academia, 11%, 7.8 avg. tools used

other, 11%, 7.1 avg. tools

Bias(Method,Affiliation) = Share(Method,Affiliation)/Share(Method) - 1

Latest KDnuggets Poll asked:The results, based on 732 voters, show that the top 10 methods are the same as in 2016 poll , although in slightly different order:The average respondent used 7.7 tools/methods, similar to 2016 poll.Next, we compared the top 16 methods in this year's poll with their share last year - see Fig. 2.We note a significant increase in Random Forests®, Visualization, and Deep Learning share of usage, and decline in K-nn, PCA, and Boosting. Gradient Boosting Machines was a new entry in 2017.Deep Learning, despite its amazing successes, is reported used by only about 20% of KDnuggets readers.The biggest relative increases, measured by (share2017 /share2016 - 1) are forWe also added new methods and here is their share in 2017:The largest decline in share of usage was forParticipation by affiliation wasNote: Only about 35 voters selected Government/Non-profit affiliation - too small a sample to analyze separately, so we merged them with the affiliation "other".Here are the top 16 methods and their bias by affiliation, computed asIf Bias positive, it means this method is used more by this group than average If negative, it is used less by this group than average.For example, support vector machines (SVM) are used by 28.7% of all respondents, but by 44.4% of Researchers, so Bias(SVM,Researcher)=44.4%/28.7% - 1 = 54.9%.Next, we examine all methods their affinity to Industry vs Academia.