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

4.4
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929 calificaciones
242 revisiones

Acerca del Curso

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 revisiones

MV

Dec 26, 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

Jun 18, 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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226 - 236 de 236 revisiones para The R Programming Environment

por Zdenek K

Nov 15, 2016

The first course of the specialization is very simple. The specialization was announced to be on an intermediate level but at the same time, you need to spend money on a very basic course with swirl assignments pretty much copying the course content. The good thing is that it includes very modern approaches to data analysis and new packages. The second course is much better, nevertheless.

por Matthew C

Oct 01, 2018

Swirl is a great idea, but each section is submitted independently of the others. You have to complete all sections in one sitting if you plan to submit electronically. I had to redo 8 of the 9 sections in week 1 for this reason.

Content-wise, the quiz in week four is significantly more difficult than the other assignments and I felt a little underprepared.

por Paul H

Oct 28, 2017

I found the final project very very difficult as weeks 1 to 3 did not cover sufficient practice of the libraries which were to be used. That having been said, the project was achievable when you spent 20 hours on it. I posted several questions to the forum and received no answers.

por Trenton H

Apr 04, 2017

There's not much substance. Also, considering there is not video the course seems very non-interactive. Its nice to see the instructors speak and work through examples. Hoping this course was just a quick primer for the R newbies.

por BenT

Jul 11, 2018

The free book R for Data Science by Garrett Grolemund andHadley Wickham is a much better structured introduction! see http://r4ds.had.co.nz/

por savvas s

Aug 28, 2017

just links to a webpage... no support from the mentors no support form coursera... you can use your money more wisely..

por Alexander D H

May 15, 2018

Felt lost during the final, the course was not well suited to the end quiz

por Arthur G

Jun 14, 2017

Too basic.

por Lakin R

Mar 13, 2020

The material of this course is useful. However, the swirl course that accompanies it is plagued with issues that date back YEARS (there are posts on GitHub and the course's discussion board from 3+ years ago with the same unresolved or barely patched issues. If I hadn't already been reimbursed for this course by my employer, I would've given up on day one.

por Robert L

Dec 02, 2019

I had to drop out because of technical issues. A week 2 issue that is documented in the bulletin board had no clear resolution. After several months of trying to figure out the issue for weeks, I determined that the course wasn't worth extra time and money being spent.

por Christopher M P

Feb 11, 2020

Final assignment may not be reviewed for weeks. If you go on to capstone, issue could extent into months. Expect to use many external resources.