Prerequisites
Outline
1
The Basics
1.1
Arithmetic Operators
1.2
Creating Variables
1.3
Logical operators
1.4
Data Types!
1.5
Data Structures
1.5.1
Vectors
1.5.2
data frames
1.6
Recap
1.7
HW
1.7.1
HW Solutions
2
Introducing the
tidyverse
2.1
Selecting data
2.2
Filtering your data
2.3
Chaining functions
2.4
Creating / manipulating data
3
Aggregation
3.1
Counting
3.2
sub-setting
3.3
Summarising
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 8
supervised learning
knn, svm, nb, rf, xgb
https://shirinsplayground.netlify.com/2018/11/ml_basics_gbm/