Week 1 of the Computing for Data Analysis course focused mostly on getting R and RStudio installed. Then it focused on some of the basics of the R language. Here are some of the topics

History of R

How to get help help()

Data types in R

numeric (real numbers)

character (strings)

integer (counting numbers)

complex (imaginary)

logical (TRUE/FALSE)

Groupings of data

vector (all the same data type)
v <- c(1.4, 2.5, 1.7)
v <- 1:10

list (NOT all same data type)
lst <- list("a", 3.5, TRUE, "word", 4+5i)

matrix (2-dimensional vector)
m <- matrix(1:20, nrow=4, ncol=5)

Factor is for categorical data
f <- factor(c("big","small","big","big"))
table(f)

Missing Values

NaN is.nan() (Not a Number)

NA is.na() (Not Available)

Reading/Writing data
d <- read.table("file.txt")
d <- read.csv("file.csv")
write.table("outFile.txt")

This course assumes a good familiarity with calculus, linear algebra, and some basic programming. Thus, if your math background is weak or needs a refresher, you may not want to take this course. However, if you have a solid math background, the course starts right into Fourier Analysis. The course topics look good, and image analysis is one of the central themes of the course. The software Matlab ($99 for student edition) is recommended, however Octave (Free) is acceptable.

The Elements of Statistical Learning textbook is available for free. It is a classic, widely-used textbooks for statistics and machine learning. Here is a far from complete list of some of the topics:

Supervised Learning

Linear/Logistic Regression

Regularization

Model Selection

Trees

Neural Networks

Support Vector Machines

Random Forests

Unsupervised Learning

Clustering

As you can see, the book is quite extensive.

Note: This book has been available for a quite a while, but I realized I have not added a link to it on my blog.