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Which methods/algorithms you used in the past 12 months for an actual Data Science-related application? (also select your employment type below)

Supervised Learning methods:
Bayesian networks
Decision Trees/Rules
K-nearest neighbors
Naive Bayes
Neural networks, "regular"
Neural networks, "Deep Learning"
Regression (Linear/Logistic)
SVM
Uplift modeling

Meta-methods:
Bagging
Boosting, including XGBoost
Ensemble methods
Random Forests

Unsupervised Learning methods:
Clustering algorithms (centroid, hierarchical, or density-based)
EM
Factor Analysis
PCA
Singular Value Decomposition
Statistics (descriptive)

Other:
Anomaly/Deviation detection
Association rules
Graph / Link / Social Network Analysis
Genetic algorithms
Optimization
Survival Analysis
Text Mining
Time series/Sequence analysis
Visualization
Other methods

Current employer:
Academia/University (Researcher)
Government/Non-profit
Industry (including self-employed)
Student
Other or unemployed


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