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Отзывы учащихся о курсе Wrangling Data in the Tidyverse от партнера Университет Джонса Хопкинса

4.8
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Оценки: 18
Рецензии: 6

О курсе

Data never arrive in the condition that you need them in order to do effective data analysis. Data need to be re-shaped, re-arranged, and re-formatted, so that they can be visualized or be inputted into a machine learning algorithm. This course addresses the problem of wrangling your data so that you can bring them under control and analyze them effectively. The key goal in data wrangling is transforming non-tidy data into tidy data. This course covers many of the critical details about handling tidy and non-tidy data in R such as converting from wide to long formats, manipulating tables with the dplyr package, understanding different R data types, processing text data with regular expressions, and conducting basic exploratory data analyses. Investing the time to learn these data wrangling techniques will make your analyses more efficient, more reproducible, and more understandable to your data science team. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course....

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1–6 из 6 отзывов о курсе Wrangling Data in the Tidyverse

автор: Glenn

15 дек. 2020 г.

Course provides a good albeit very cursory overview of data wrangling tools in the tidyverse. However, the bulk of my time was wasted on a quiz question which was unclear/had wrong wording. As the figure is supposed to be keyed in (not multiple-choice), it was frustrating trying to guess what the question actually wanted.

автор: m s

30 дек. 2020 г.

Loved it! I really liked that it was all reading and based in real examples! Thank you!

автор: Long T V

24 апр. 2021 г.

Excellent course! I've learned so many useful R techniques/codes!

автор: Stefan M

2 окт. 2021 г.

Great course with clearly understandable lectures.

автор: Moses O

9 дек. 2020 г.

Magnificent

автор: Adaman Y

25 сент. 2021 г.

great