# Chapter 1 Getting started

Since this chapter does not deal with statistics, I have decided to skip this chapter altogether.

### Before reading DA4LS with R

The book gives a brief introduction to R. But I do not think this is enough. The codes in the later part of the book can get quite complicated (especially from Chapter 6). If you do not know R (or have no coding experience), I suggest you read Chapters 5, 15, 19-21 of R for Data Science by Hadley Wickham and Garrett Grolemund; these chapters discuss data transformation, factors, custom functions, vectors and iterations (ex. `for`

loops, `sapply`

). These concepts are deeply embedded in the codes throughout the book.

There is also matrix algebra (ex. singular value decomposition) in this book (from Chapter 4). However, the book reviews matrix algebra briefly. If you have not taken a course in linear algebra, I suggest that you spend some extra time reviewing key concepts such as matrix multiplication, dot product, orthogonal matrix, inverse matrix and square matrix before reading Chapter 4. Here is a link to Khan Academy. When you are going through Chapter 8, you might also have to look up for extra resource on principal component analysis; I recommend this Youtube video by StatQuest.

Also, Chapter 10 is a continuation of Chapter 8, so I suggest you read Chapter 10 (batch effect) before Chapter 9 (machine learning). Good luck!