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The R Programming Environment

This course is a part of Mastering Software Development in R, a 5-course Specialization series from Coursera.

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.
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Johns Hopkins University

Rating 4.0 based on 237 ratings
Length 5 weeks
Starts May 11 (3 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brooke Anderson
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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What people are saying

According to other learners, here's what you need to know

very good in 16 reviews

Very good introduction to R and certain packages such as dplyr and grepl.

A really good introduction to R. very good start English is not my native language so i had troubles with tasks, because it is sometimes hard to understand what you have to do.

The Week 02 Swirl assignment was more time consuming than the others and the Week 04 quiz which required hands on problem solving was very good and an excellent review of the materials.

Very good overall.

very good introduction to the R programming language, with holistic introduction to updated R packages written by Hadley Wickham.

Thanks Very Good I like the swirl exercises, but found the text lessons to be very short.

Very good starting course, covers all the basics.My 2 cents: I would prefer more tests like the last one than the swirl lessons, they're more challenging thus you learn more.

Nicely structured and organized Very good material and good exercise!

This course was a very good and powerful intro in data wrangling and tidy data.

Very good introduction to R capabilities.

Very good course.

A very good course to read and get the valuable content of R language.

It is a very good course for new learners.

This package /process is really buggy and causes needless grief especially in Lesson 2.The content of this course is A+++ Brilliant course to get the basics of data wrangling very good course.

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data science in 11 reviews

Would make it much better if it had video tutorials however Felt lost during the final, the course was not well suited to the end quiz Nice Learn basics and some good data science tools.

Except the last programming assignment in week 4, which deals with large dataset that my computer had tough time reading, the course if pretty good and had provided me a basis for specialization in Data Science.

Also a great refresher course after taking the Data Science Specialization.

!...really helpful for building data science concept through R Programming.........Really salute for hardworking of instructor..................!!!

I would recommend to take the other R programming course from the Data Science track before.

Nonetheless I definitely learned a few practical things that will up my data science game (which can always use upping).

If you have done the Data Science Specialization prior, this is a lot of review.

Good course, i learned a lot on R. T This is a refresher course for someone who already knows R. For anyone starting to learn R and Data Science, Data Science Specialization is a good curriculum to start with.

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.

The free book R for Data Science by Garrett Grolemund andHadley Wickham is a much better structured introduction!

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data manipulation in 11 reviews

I really liked the format of the Quiz for Week 4 - there is a dataset given and questions requiring you to do your own data manipulation/calculation to come to an answer (as opposed to the other weeks requiring swirl lessons, which weren't as fun/challenging).

A great introduction to the R environment and data manipulation with the tidyverse.

Basic data manipulation is invaluable when using the scale matrices, the tools that this course has provided me with has had a direct impact on how I conduct my own research and I have changed how I now 'tidy' my data prior to analysis It is a good starting point to utilize the advanced R programming techniques It was very dificul, i think yoou need to improve the example you give the the students and be more interactive Good content, a lot of work loaded in the quiz of the last week, beware of this.

However the swirl lesson #12 on Data Manipulation and quiz of week #4 on Reading and Summarizing Data offer a huge difficulty spike in comparison with the rest of the assignments.

By the end of it, you do become proficient in reading data from different formats into R and in performing basic data manipulation routines using cutting edge packages.

简单易学,很适合初学者和基础学习。 This course starts out with the basics of R programming and the use of the tidyverse set of package that are essential for data manipulation.

This course elevate my experiences on advanced data manipulation.

It taught me very useful data manipulation techniques such as piping and other aspects of the tidyverse.

This is for the students who want to learn and practice the basic and some intermediate concepts of data manipulation.

Very practical course with plenty of practices to get you started data handling in R. Course focus on Tidyverse series of packages which come in handy when dealt with data manipulation.

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final quiz in 8 reviews

Finally, the final quiz material had spaces in the column headings which was fixable but added a level of monotony and inconvenience that was not needed.

My advice - rewrite final quiz and make it more easy for understanding.

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

Also, the final Quiz was much more difficult than the readings/practice that came before it.

However, it could be better if there is a R script writing the right code for the final quiz.

The final quiz was extremely challenging, as it should be, and very useful.I give this course six stars and take one away for the automated upload of answers from R (the swirl package).

Also, the final quiz was disproportionally difficult in comparison to what was taught during the weeks leading up to week 4.

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very helpful in 6 reviews

The course was very helpful on covering the basics.

the forum is not very helpful.

Very helpful information provided that I will use on a regular basis.

I think this class is very helpful!

In addition, the possibility of deciding your own work schedule is very helpful when you have to do other stuff from the university.

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Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

AD, Data Science $47k

Associate Data Science Supervisor $55k

Science writer / data analyst $63k

Genomic Data Science Programmer $75k

Volunteer Director of Data Science $78k

Expert Data Science Supervisor $79k

Supervisor 1 Data Science Supervisor $91k

Guest Director of Data Science $101k

Data Science Architect $105k

Head of Data Science $131k

Assistant Director 1 of Data Science $133k

Owner Director of Data Science $149k

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Coursera

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Johns Hopkins University

Rating 4.0 based on 237 ratings
Length 5 weeks
Starts May 11 (3 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Brooke Anderson
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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