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%
来自高级 R 语言程序设计的热门评论
the last peer review problem is much too hard for what I learned from the course material, if there is a more specific instruction for the assignment will be better.
For me the course provided a quick and easy introduction to the 'purr' package as well as clarity on the current state of R's object oriented programming system.
Good course, nothing much to say, definitely teaches the use of R, not quite sure it is "advanced" but I guess...
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".
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.