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Opiniones y comentarios de aprendices correspondientes a The R Programming Environment por parte de Universidad Johns Hopkins

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1,111 calificaciones
305 reseña

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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....

Principales reseñas

MV
25 de dic. de 2018

Very Very Rigorous Course for a beginner on R language and because of its nature, after completing just one course, I feel like I have gained a lot of knowledge and also familiarity with R language.

KV
17 de jun. de 2019

A very good course to read and get the valuable content of R language. This is for the students who want to learn and practice the basic and some intermediate concepts of data manipulation.

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151 - 175 de 301 revisiones para The R Programming Environment

por Yifei L

13 de oct. de 2018

Great class!!

por JEEWESH K J

27 de oct. de 2017

Great Course

por Le D A

19 de mar. de 2020

Very useful

por Arthur C

10 de feb. de 2018

Nice course

por Sameh M A

16 de feb. de 2021

Very cool!

por David O O

20 de oct. de 2020

Very good.

por Francisco A M

22 de feb. de 2018

Excelente!

por Rodrigo G G

6 de mar. de 2017

Fantastic!

por Dante O

23 de sep. de 2020

Muy bueno

por Marcelo B

4 de ago. de 2017

Very Good

por Rebecca A D

24 de sep. de 2017

good one

por Imagine R

17 de ago. de 2020

nice

por Ganapathi N K

13 de may. de 2018

Nice

por Ben S

14 de may. de 2019

T

por Dmitry S

1 de oct. de 2016

+: I reached my goal for the course and now I understand a bit about R. I succeeded to pass within much shorter time than anticipated course duration. The course certificate is posted to my Linkedin profile.

-: No human mentors on the course discussion forum - all questions answered by other students. Automatic tests in swirl are too restrictive and do not accept perfectly correct student solutions slightly different to those anticipated by the authors. Week 2 assignment is much different from the reading material. Nothing taught about charting in R.

Overall comment: I think it is good value for money.

por wally

25 de ene. de 2017

All in all good stuff. A couple of comments:

Swirl grading should be a little more flexible; sure cut is more succinct that nested ifelse's, but there's more than one way to skin the data-analytic cat in R, as I'm sure y'all are aware.

Also, I recommend more emphasis on data tables. I use them exclusively due to the dramatic performance improvement over data frames. And in my brief experiments, dplyr and tidyr commands worked on them too.

This first course was review for me. Nonetheless I definitely learned a few practical things that will up my data science game (which can always use upping).

por Bryan D

14 de ene. de 2018

There are a few areas of the course that are not fully explained and cause extra time trying to 'figure it out'. I am not referring to code/learning, but the logistics of the class. However, there are a few questions in assignments & quizzes that refer to things that were not particularly explained either in the lecture or in the book. I understand self study is beneficial, but I start the course with the understanding that questions will be based off of lecture or the book. If additional resources are needed, then that should be expressly stated in the lecture or book.

por Taz P

23 de ene. de 2019

This course has a perfect combination of theory and hands-on exercises. I've been doing a few courses in Data Science on Coursera and I have found this method the best at least for me personally. I will probably carry on doing these courses. On the negative side, the student forum is practically dead, don't count on getting any help there. Coursera support have been very difficult and I had to wait a while to get my certificate. In the meantime Coursera charged me for an extra month (after I'd completed the course).

por Kevin D

10 de jul. de 2017

Une initiation intéressante et complète à R qui a l'avantage de donner des bons réflexes pour coder rapidement. Le caractère interactif des exercices de programmation est un gros plus. Attention : il n'y a pas de vidéos hormis la vidéo d'introduction. On peut aussi avoir des difficultés de syntaxe susceptibles de nous bloquer un bout de temps, notamment sur des fonctions où la documentation est un peu aride (je pense à cut et à spread sur lesquelles j'ai bien passé 1/4 du temps que j'ai consacré au total à ce MOOC).

por Rafael d S P

3 de jun. de 2020

The course is good, it concisely teaches the basics of R and it is based on chapter 1 of the book Mastering Software Development in R, so there are almost no videos, just readings. I recommend it if you have little experience with R and want a quick upgrade. However, there are some errors and typos in the material, including the final quiz. Besides, forums are mostly supported by students, the answers from mentors were from some years ago, and I have seen people with difficulties to contact the support.

por Francisco D I T

30 de sep. de 2019

I found this course quite interesting because sometimes we learn about modeling techniques, but we forget one of the most important things: how to write a good script by applying useful packages or functions to read and manage datasets. It has been an easy course and I recommend it to people who want to start basic reading and managing data with R-project. In addition, the possibility of deciding your own work schedule is very helpful when you have to do other stuff from the university.

por Jhosse P M R

4 de feb. de 2017

Excelent course to start with the basics of R language. It gives a modern introduction to R programming avoiding traditional topics like loops and conditionals keeping them for more advanced programming topics. It also covers almost all tidyverse packages with a shallow introduction to the most common packages.

I have acomplaint. The dataset for the final quiz was outdated, I managed to pass the quiz guessing variable names.

por Patrick R

9 de dic. de 2018

The course book is good and a nice resource. Swirl lessons were also really nice. They are interactive and helped becoming familiar with the functions. However, video tutorials on real datasets like the one from week 4 would be helpful. Also, the final quiz was disproportionally difficult in comparison to what was taught during the weeks leading up to week 4. Overall a good course that provides a solid introduction to R.

por Andy W

12 de abr. de 2021

The exercises and examples led to a better understanding than many course I've taken. The swirl interactive sessions helped to understand it well. It is sometimes frustrating to get the right answer, but but do it in the wrong way and not have it count. Also there is the issue of having old versions of tidyverse in course (e.g., gather vs pivot_longer), but it would be pretty hard to keep the course constantly updated.

por Aubrey L

16 de ene. de 2020

Some frustrating issues with Swirl functionality, but the discussion boards are helpful for troubleshooting fixes. Seems to cover the majority of the basics I previously learned in the R section of an in-person, for-credit Quantitative Methods of Management course I took at my college, plus it included a few additional useful topics. I appreciate that it provides links for additional learning. All around solid course.