At this moment I am following two similar MOOCs, one on Coursera (Exploratory Data Analysis, part of the Johns Hopkins Data Science Specialization) and one on Udacity (Data Analysis with R, part of the their Data Analyst Nanodegree). At first glance, the two should deal with roughly the same subject, however I would rather say that they work at their best when they are used to complement each other.
The Coursera Data Science Specialization is broken up in coursers which represent chunks of 4 weeks of study. In this respect, the Exploratory Data Analysis fits well after the R Programming and the Getting and Cleaning Data. The 4 weeks are enough time to get around the exercises, and the focus is on building up on those two courses and adding a well structured idea of how data can be quickly visualized in R, about the three main plotting systems (Base, Lattice and Ggplot2) and then goes on about the clustering algorithms and their representations and about dimensionality reduction and use of colours in R. This is all good, the course is closed by two sizeable examples, rather useful. I finished taking this course this week.
The Udacity course instead is something that must be coded along, it is composed of brief videos and whenever the instructors thinks it is the right moment to do so, there is a quiz which serve the purpose of a checkpoint. I find this a very good idea. The Udacity instructors also include references to reading material and usually do not leave anything for granted (so, for instance the difference btween histograms and bar plots is explained). On the other end, they do not care about the type of graphics system to use, they just use Ggplot2. The chapters are organized so that the focus is about exploring one variable, two variables or many variables and the course closes with an example. I have not yet finished to take this course, and I am not going for the Nanodegree.
What I can suggest is that if you are really interested about doing exploratory data analysis with R, you take both these courses. The first one (does not matter which one) will pave the way for the other which will be easier but still add something to your knowledge as neither are complete.
