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.
Этот курс входит в специализацию ''Специализация Mastering Software Development in R'
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Специализация Mastering Software Development in RУниверситет Джонса ХопкинсаОб этом курсе
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Попробуйте Coursera для бизнесаПриобретаемые навыки
- Data Manipulation
- Regular Expression (REGEX)
- R Programming
- Rstudio
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Программа курса: что вы изучите
Basic R Language
Basic R Language: Lesson Choices
Data Manipulation
Data Manipulation: Lesson Choices
Text Processing, Regular Expression, & Physical Memory
Text Processing, Regular Expression, & Physical Memory: Lesson Choices
Large Datasets
Рецензии
- 5 stars59,49 %
- 4 stars25,08 %
- 3 stars7,49 %
- 2 stars3,22 %
- 1 star4,70 %
Лучшие отзывы о курсе THE R PROGRAMMING ENVIRONMENT
The content of the course is very interesting and useful, for those who want to learn a tool that allows them to perform data analysis, just a small detail that is in one of the assignments
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.
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.
Great Introduction, may we worth clarifying that for Data Manipulation the script must be saved before entering submit() as you cannot make progress.
Специализация Mastering Software Development in R: общие сведения
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
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Можно ли получить финансовую помощь?
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