1. Introduction Tidyverse
1.1 Data wrangling
2.2 Data Visualization: ggplot2
2.3 Grouping and summarizing.
2. Text Mining.
2.1 Main concepts.
2.2 Word clouds.
2.3 Term by document matrix.
2.4 R implementations and applications.
3. Data visualization. Metric Multidimensional Scaling, Correspondence Analysis, Biplots.
3.1 Metric Multidimensional Scaling.
3.2 Biplots.
3.2 Perceptual Mappings.
4. Cluster Analysis. Hierarchical Methods, k-means and mixture models.
4.1 Bottom up hierarchical clustering algorithms.
4.2 k-means and related algorithms.
5. Information Theory and classification trees.
5.1 Information theory.
5.2 Classification trees algorithms.
5.3 Real case: credit scoring.
5.4 Case studies.
6. Association Rules.
6.1 Main concepts and algorithms.
6.2 Complete example with R code.
6.3 Case studies.
7. Deep Learning.
7.1 Support Vector Machines.
7.2 Neural Networks for classification.
7.3 Neural Networks for regression.
8. Case Studies.
8.1 Comprehensive real cases involving all the studied techniques.