开始时间: 04/22/2022 持续时间: 4 weeks
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所在平台: CourseraArchive 课程类别: 信息，技术与设计 大学或机构: Johns Hopkins University(约翰•霍普金斯大学) 授课老师： Roger Peng |
课程主页: https://www.coursera.org/course/repdata
课程评论：没有评论
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.
In this course you will learn to write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, and organize a data analysis so that it is reproducible and accessible to others.
Learn the concepts and tools behind reporting modern data analyses in a reproducible manner. This is the fifth course in the Johns Hopkins Data Science Specialization.
