Exploratory Data Analysis

The first 2 weeks of the course provide a thorough overview of plotting in R using the base graphical package, the lattice package and the ggplot2 package. Week 3 takes a sudden detour into data clustering and the fairly advanced topics of principal components analysis and single value decomposition only jump back to plotting with a section on color. The clustering section seems a little about of place since there is not any introduction explaining the purpose of clustering. What's worse the SVD and PCA sections require a fairly high level of linear algebra knowledge to understand, which are not prerequisites for this course. I suspect that section will leave may students scratching their heads. Week 4 consists of 2 case studies where the professor shows you how to perform an exploratory analysis on a couple different data sets.

This is the fourth course in the Data Science specialization. The course covers exploratory analyses in R, primarily making figures using the three most common packages: base R, lattice, and ggplot2. The instructors also manage to throw hierarchical clustering, k-means, and pca into the 3rd week of the course, which seems a little odd as these topics might be better left for the machine learning course. The course ends with a peer-graded course project, similar to other courses in the specialization.

I found this course to be fairly useful, on par with the preceding courses but perhaps a bit worse than Getting and Cleaning Data. As with the previous courses, I front-loaded my work and finished fairly early, in part because I was taking Reproducible Research and Bioconductor for Genomic Data Science concurrently. I found the quizzes and project to be relatively straightforward, although again the peer grading is somewhat less-than-useful.

Overall, three stars. A reasonable introduction to graphing in R, with some basic clustering and dimension reduction strategies tacked on to the end. Experience with R at the level of R Programming is almost certainly required, as stated in the course prerequisites.