1. Introduction.
2. Data collection, sampling and preprocessing.
2.1. Types of data.
2.2. Sampling.
2.3. Data visualization tools.
2.4. Missing values.
2.5. Outlier detection and treatment.
2.6. Data transformations.
2.7. Dimension reduction.
2.8. Application: Risk management in the stock market.
3. Supervised learning: regression.
3.1. Linear and polynomial regression.
3.2. Cross-validation.
3.3. Model selection and regularization methods (ridge and lasso).
3.4. Nonlinear models, splines and generalized additive models.
3.5. Application: credit-scoring prediction.
4. Supervised learning: classification.
4.1. Bayes classifiers
4.2. Logistic regression.
4.3. K-nearest neighbors.
4.4. Random forest.
4.5. Support-vector machines.
4.6. Boosting.
4.7. Application: Credit risk.
4.8. Application: Fraud detection.
4.9. Application: Bankruptcy prediction
