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.
ジョンズ・ホプキンズ大学（Johns Hopkins University）
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 stars59.58%
- 4 stars25.02%
- 3 stars7.52%
- 2 stars3.23%
- 1 star4.63%
THE R PROGRAMMING ENVIRONMENT からの人気レビュー
A very good course to read and get the valuable content of R language. This is for the students who want to learn and practice the basic and some intermediate concepts of data manipulation.
Good to learn the possibilities in the R environment. In the end you learn most by applying it to your own projects (with a lot of help in available documentation or via internet sea).
Very good overall. I feel that the first weeks are too easy relative to the last week - more involved practice and exercises would have been good before getting to the final quiz.
I liked the swirl() package a lot, made me jump into writing codes into RStudio straight away. My only wish was to have some videos that would push me further
Mastering Software Development in R専門講座について
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.