Reproducible Research

The first 2.5 weeks of lecture material is great. It provides a well-organized overview of how to create reproducible research in R using R markdown and the knitr package, taking plenty of time to talk about best practices. Thankfully, Roger Peng has added in a little box with his face in at as he talks over his slides for many of his videos, which makes the content a lot more engaging than it is in some of the other John Hopkins courses that only have voiceovers.

The final 1.5 weeks of lecture video material is not as useful or engaging and seems a bit lazy in that week 4 takes the form of recordings of lectures given sometime in the past. The videos in second half of week 3 only have voiceovers and they have an echo to them that makes them hard to listen to.

Reproducible Research is the fifth course in the Data Science specialization, and the last course in what could reasonably be considered the basic R introduction portion of the series. Following this course, students move into Statistical Inference, Regression Models, and Practical Machine Learning, courses which are more about analytical techniques than basic programming skills.

The idea behind reproducible research is to inform students about the reproducibility crisis in science and to give them tools to make their analysis reproducible. This is something that has long had importance in programming but has only recently been given much credence in experimental sciences, so this course is relatively timely for those of us in the latter fields. The course covers R Markdown and knitr as a method of producing reports with integrated analyses, and also covers RPubs for communicating results. Overall this is probably the most useful and important course in the first part of the specialization. The course itself is easy, although the two projects (week 2 and week 4) can be very time-consuming depending on how much effort you choose to expend. I went for the option of providing the minimum to meet the stated requirements, and I still received 100% on my peer feedback, although this strategy can be risky, so proceed with caution if you choose to do this.

Overall, four stars. The best course in the first half of the specialization, and it gives a good overview of strategies to disseminate your analyses and results in an understandable and reproducible way. I took this concurrently with Exploratory Data Analysis, but some of the material from that course is useful here, so make sure you have enough time to complete both if you choose this route.