1.Network data Analysis
1.1. Introduction
1.2. Grafh types: Direct and Indirect
1.3. Visualization and descriptive analysis of Network data
1.4. Graphic layout
1.5. Connections and Contiguity Matrixes
1.6. Metrics and taxonomy of Network data
1.7. Use of Network data in Classification and Forecasting
1.8. Network data collection with R
1.9. Applications
2. Advanced Regressive Methods
2.1. Local polynomial regression: non-parametric regression
2.2. Selection of variables based on the penalization of regression models
2.3. LASSO
2.4. Quantitative regression
2.5. Applications
3. Cluster Analysis
3.1. Introduction
3.2. Distances between Units and between Groups
3.3. Hierarchical clustering
3.4. Dendrogram: visualization of the aggregation process
3.5. Limits of Hierarchical Clustering
3.6. Non-Hierarchical Clustering The k-Means Algorithm
3.7. Applications
4. Time Series
4.1. Introduction
4.2. Collection Time Series data with R
4.3. Components of a time series
4.4. The ARIMA models: Identification and Estimation
4.5. Use of the ARIMA models for prediction
4.6. Other forecasting methods
4.7. Applications
5. Factor Analysis and Structural Equation Models
6. Correspondences Analysis
7. Multidimensional Scaling