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
This course is very helpful in terms of not only doing the analysis but also getting to know the finer nuances of making a structured markdown document for future reproducible.
First week has an assignment that requires knowledge from the second week. It would be better for the course if both assignments has two weeks for accomplishment.
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 :)
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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