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.
Informações sobre o curso
Habilidades que você terá
Universidade Johns Hopkins
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 stars68,65%
- 4 stars22,95%
- 3 stars5,73%
- 2 stars1,62%
- 1 star1,02%
Principais avaliações do PESQUISA REPRODUTÍVEL
If you are at university (PhD student, academic, researcher, etc.) then you kind of know most of the "theory". However, practising R was a huge plus (personally, I liked the Week 4 task).
Very informative and enjoyable class. The importance of reproducible research is stressed clear and concisely, Roger D. Peng does a great job of explaining the material.
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
Enjoyed learning about rMarkdown, caching, and RPubs. Was also able to spend time plotting and aggregating data in different ways. Didn't enjoy cleaning data too much :)
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