Which methods/algorithms did you use for data analysis in 2011? [311 voters] | |
Decision Trees/Rules (186) | 59.8 % |
Regression (180) | 57.9 % |
Clustering (163) | 52.4 % |
Statistics (descriptive) (149) | 47.9 % |
Visualization (119) | 38.3 % |
Time series/Sequence analysis (92) | 29.6 % |
Support Vector (SVM) (89) | 28.6 % |
Association rules (89) | 28.6 % |
Ensemble methods (88) | 28.3 % |
Text Mining (86) | 27.7 % |
Neural Nets (84) | 27.0 % |
Boosting (73) | 23.5 % |
Bayesian (68) | 21.9 % |
Bagging (63) | 20.3 % |
Factor Analysis (58) | 18.7 % |
Anomaly/Deviation detection (51) | 16.4 % |
Social Network Analysis (44) | 14.2 % |
Survival Analysis (29) | 9.32 % |
Genetic algorithms (29) | 9.32 % |
Uplift modeling (15) | 4.82 % |
Did you use analytics in the cloud, Hadoop, EC2, etc in 2011? | |
Yes | 14% |
No | 86% |
Employment type: | Percent all | Avg Num Algorithms |
Industry analyst/consultant (172) | 55.3% | 6.3 |
Academic researcher (85) | 27.3% | 5.1 |
Student (37) | 11.9% | 4.3 |
Government/Other (17) | 5.5% | 5.0 |
Regional breakdown is
- US/Canada, 40.2%
- Europe, 37.6%
- Asia, 10.3%
- Latin America, 5.8%
- Africa/Middle East, 3.2%
- Australia/NZ 2.9%
N(Alg,Ind_Gov) / N(Alg,Aca_Stu)Thus algorithm with affinity 1.5 is used 50% more in Industry/Government than by Academic Researchers or students, and the algorithm with affinity 0.6 is used only 60% as much in Industry.
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N(Ind_Gov) / N(Aca_Stu)
The most "industrial" algorithms ( with the highest Industry / Gov "affinity") are:
- Uplift modeling, INF (no academic users)
- Survival Analysis, 2.47
- Regression, 2.00
The most "academic" algorithms ( with the lowest Industry / Gov "affinity") are:
- Genetic algorithms, 0.60
- Support Vector (SVM), 0.66
- Association Rules, 0.83
Algorithm | Academic/ Student Affinity |
Industry / Gov Affinity |
---|---|---|
Uplift modeling | INF | |
Survival Analysis | 2.47 | |
Regression | 2.00 | |
Visualization | 1.55 | |
Statistics | 1.54 | |
Boosting | 1.50 | |
Time series/Sequence analysis | 1.48 | |
Bagging | 1.39 | |
Factor Analysis | 1.32 | |
Anomaly/Deviation detection | 1.29 | |
Text Mining | 1.27 | |
Decision Trees | 1.20 | |
Neural Nets | 1.16 | |
Clustering | 1.14 | |
Ensemble methods | 1.08 | |
Social Network Analysis | 0.93 | |
Bayesian | 0.92 | |
Association rules | 0.83 | |
Support Vector -SVM | 0.66 | |
Genetic algorithms | 0.60 |