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 |