In an excellent move to bring reproducible research to everyone for free, Johns Hopkins University now offers a four-weeks course on Coursera. The course provides videos and exercises to learn statistical analysis tools that allow others to replicate your work easily. The course starts May 5.
The new coursera course Reproducible Research teaches the “concepts and tools behind reporting modern data analyses in a reproducible manner.” The course description defines reproducibility as:
Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. (…) Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary.
The instructor, Roger D. Peng from Johns Hopkins University, aims at providing “literate statistical analysis tools which allow one to publish data analyses in a single document” so that others can reproduce the results.
I signed up for the course and had a look at the introductory video. Peng explaines that as data sets become more complicated, it becomes harder and harder to reproduce the work. He says that reproducibility is an important aspect of any data analysis because it communicates what you have done.
The course covers:
- concepts and ideas
- structuring your data analysis
- tools such as R Markdown, knitr, RPubs
- and two case studies.
All videos are freely available online already (to get a certificate the course costs $49). I like that you can download the videos and watch them whenever you want. There are deadlines for the assignments if you want to do the practial exercises as well.
This is an excellent way to get familiar with reproducible research tools!