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.
- 5 stars59.68%
- 4 stars24.97%
- 3 stars7.44%
- 2 stars3.24%
- 1 star4.64%
來自THE R PROGRAMMING ENVIRONMENT的熱門評論
This course gave a great review of R. It also did a great job of highlighting the power of the tidyverse library for preparing data for analysis.
A thorough course that covers a lot of efficient data manipulation styles within the R environment. I learned a lot of neat tricks that help with quick analysis of large data frames.
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.
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
關於 Mastering Software Development in R 專項課程
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.