This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.
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.
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来自THE R PROGRAMMING ENVIRONMENT的热门评论
Very good starting course, covers all the basics. My 2 cents: I would prefer more tests like the last one than the swirl lessons, they're more challenging thus you learn more.
The whole course was easy to follow except for the last questions of the last exam where the merged data set results into a null data frame after filtering.
The R Programing Environment is very supportive environment, and I appriciate the guideline and the materials. futhermore, the course was good begining. Thanks
Great Introduction, may we worth clarifying that for Data Manipulation the script must be saved before entering submit() as you cannot make progress.
关于 Mastering Software Development in R 专项课程
R is a programming language and a free software environment for statistical computing and graphics, widely used by data analysts, data scientists and statisticians. This Specialization covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing and scaling useful data science results and products.