Statistical Data Mining

Course content

Descriptive and predictive mining. Data preprocessing: cleaning transformation. outlier detection, missing data imputation. Dimension reduction, Principal Component Analysis (PCA). Sampling, oversampling. Exploratory data analysis (EDA). Clustering methods: partitioning, hierarchical, density-based, model-based. Predictive modeling. Regression. Variable selection. Robust and nonlinear regression. Nonparametric regression. Classifiers. Logistic regression. Decision trees. Random Forest. Model evaluation and validation. Real-life applications using recent available software.