This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization’s mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.
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 stars58.96%
- 4 stars22.58%
- 3 stars10.39%
- 2 stars2.86%
- 1 star5.19%
來自高级 R 语言程序设计的熱門評論
It is a good course that forced me to understand the s3 and s4 object of R and have gained an appreciation of "methods belonging to functions not belonging to objects".
Great course but would prefer more video lectures versus text based lectures. Otherwise, a great course to help build out the foundations of R programming.
Good course, nothing much to say, definitely teaches the use of R, not quite sure it is "advanced" but I guess...
The final homework assignment is tough if you are a newcomer to R. It is sink or swim time. Worth it if you can get through it.
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.