This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. 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. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
About this Course
Skills you will gain
Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
- 5 stars68.64%
- 4 stars22.95%
- 3 stars5.71%
- 2 stars1.64%
- 1 star1.04%
TOP REVIEWS FROM REPRODUCIBLE RESEARCH
This is a necessary evil. You can try to do the other classes in the specialization without it, but learning to use R markdown well is hard with out this or a similar class
My favorite course, at least it gives me an argument why scripted statistics is awesome and can be applied to a number of data related activities. Recycling chunks of code has proven useful to me.
it shows how to better communicate one analysis and i have learnt a lot from it. the lectures should be updated as some details and figures were irrelevant a this time
I took this course as part of the Data Science specialization without any real expectation and realized that this subject is probably one of the most important in data analysis.
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