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.
Offered By
Johns Hopkins UniversityAbout this Course
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Try Coursera for BusinessWhat you will learn
Organize data analysis to help make it more reproducible
Write up a reproducible data analysis using knitr
Determine the reproducibility of analysis project
Publish reproducible web documents using Markdown
Skills you will gain
- Knitr
- Data Analysis
- R Programming
- Markup Language
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Syllabus - What you will learn from this course
Week 1: Concepts, Ideas, & Structure
Week 2: Markdown & knitr
Week 3: Reproducible Research Checklist & Evidence-based Data Analysis
Week 4: Case Studies & Commentaries
Reviews
- 5 stars68.65%
- 4 stars22.93%
- 3 stars5.72%
- 2 stars1.64%
- 1 star1.03%
TOP REVIEWS FROM REPRODUCIBLE RESEARCH
While I'm pretty sure this course is VERY important for researchers, it is not very useful for my area (IT) and I would like to know this before taking the course. Thank you.
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
You will learn how to use a very valuable tool in this class; its name is R Markdown. Besides Prof. Peng explains very well the importance of reproducible research. Nice course!
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).
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