Prerequisites
- Outline
1 The Basics
2 Introducing the tidyverse
3 Aggregation
4 Visualization
5 Ok, but where? (GIS)
6 statistical modelling (Regression)
7 Data preprocessing
8 supervised learning
9 Unsupervised learning
10 text-mining / natual language processing (nlp)

R for Social Science

Chapter 7 Data preprocessing

center, scale, standardize, normalize, cross validation, dimensionality reduction, feature selection