What are MOOCs?

How do I register?

To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.

How do these MOOCs or free online courses work?

MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.

13 out of 14 people found the following review useful

3 years ago
**completed** this course.

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 p
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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.

8 out of 9 people found the following review useful

3 years ago

A painful, dull offline course on plotting & clustering in R slapped online with minimal conversion like the rest of JHU's execrable Data Science specialisation*. Hard only due to the appalling pedagogy. (Have these guys heard of labs? Apparently not...)

*Which, tragically, is apparently one of Coursera's top moneyspinners. Sigh.

*Which, tragically, is apparently one of Coursera's top moneyspinners. Sigh.

6 out of 6 people found the following review useful

3 years ago
**completed** this course.

Another boring course you'll have to slog through. It's half learning a few things about making plots, half topics that been better covered elsewhere (k-mean). You can actually graduate those courses with horrible programming. As usual you'll learn more by surfing stack-overflow than by the videos. I've done half the assignments before looking at the vids.

1 out of 1 people found the following review useful

a year ago

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 se
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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.

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.

4 out of 4 people found the following review useful

2 years ago
**dropped** this course.

A boring and pointless money-generating vehicle from JH. And yes - reviews should be at least 20 words - I wonder if I find a way around that.

1 out of 1 people found the following review useful

2 years ago

This is a good starting point for any data analysis work, and the course covers the basics, and a bit more, rather well. It's a bit light on what you should do with the information you gather from your data exploration though.

a year ago
**completed** this course.

Quite good, quite basic for those who want to review their knowledge. Should be good for those with no previous experience.

0 out of 1 people found the following review useful

0 out of 1 people found the following review useful

0 out of 1 people found the following review useful

0 out of 1 people found the following review useful

0 out of 1 people found the following review useful

0 out of 1 people found the following review useful

0 out of 1 people found the following review useful

1 out of 4 people found the following review useful

3 years ago
**completed** this course, spending **5 hours** a week on it and found the course difficulty to be **easy**.