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R Programming

This course is a part of Data Science, a 11-course Specialization series from Coursera.

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
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Johns Hopkins University

Rating 4.1 based on 2,515 ratings
Length 5 weeks
Starts May 11 (13 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

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

data science specialization in 14 reviews

This is the second course in the Data Science specialization, and it is the foundation course for the rest of the classes.

Best course for learning R but everyone must complete the data science specialization courses for better use of it :) Very theoretical; massive gap between the course (theory) and the tests (application).

Perhaps the material here will be reinforced in later courses in the data science specialization.

If you're looking for a course that will help you grasp R Programming, I would strongly recommend going through the entire Data Science Specialization.

If you are doing the Data Science specialization, you are just getting warmed up... :) It's very good, but activities a lot and few time.

EASY Some though job to do Training is good, but assignments are even more interesting to work on.. in course purchase make th Very useful and challenging introduction into R the course is absolutely not for beginner who has little knowledge of R even they are interested in data science specialization... this should be made clear to avoid waste of time, efforts and budget.. not worth subscription Great design!

I am doing Data Science Specialization.

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lexical scoping in 11 reviews

Secondly, the difficulty of the topics is changing a lot along the way: some topics are quite demanding (lexical scoping) and then next something basic (debugging) is explained for a long time.

Lexical scoping was particularly difficult to understand at first, and I also had to rewatch it a few times, but it did help me a lot in actually learning the language.

I still don't know what I would ever use the Lexical scoping example for.

Swirl is a good option but learning it first time will make a great difference Learnt a lot in R especially Lexical Scoping, Datatypes, vector operations, loops and apply commands.

Still in the dark mostly about the true purpose of lexical scoping in practical situations.

Good base Generally good, but I didn't like the exercise on lexical scoping.

very rewarding course Very good course, however I find some of the chosen covered topic maybe too specialized (ie lexical scoping for example)...however maybe it will make more sense with time.

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dr. peng in 11 reviews

Reading poor slide content (poorly) is not teaching, Dr. Peng.

Although Dr. Peng's book provided a great deal of clarity that did not appear in the lectures and related material.

Thank you, Dr. Peng.

Hopefully, Dr. Peng or someone from JHU will see this feedback and be interested in making improvements to the curriculum.

Thank you Dr. Peng!

Dr. Peng is great at teaching, and the lectures are not hard to follow.

Also, Dr. Peng makes gross burping/drooling noises in the video from time to time.

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highly recommended in 9 reviews

Highly recommended.

Excellent course to start with R. Highly recommended.

Highly recommended, but not for the unitiated without conviction or the faint of heart.

The videos do have links where you can find more reading material, when needed, although highly recommended.

..highly recommended...

Highly recommended to start programming in R. The new platform Sucks!!

Highly recommended!

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stack overflow in 8 reviews

You will learn a lot, but as others have said, most of what you learn is self taught from google and stack overflow and browsing the forums.

I spent countless hours searching through stack overflow and the course forum to no avail.

But as a starting R programmer I found myself stuck on some of the materials without knowing where to turn except for stack overflow.

The Assignments do not require just what is being taught, and demands a lot of google and stack overflow research in order to solve the problems.

It is up to the student to figure out through stack overflow, youtube, and other search result to figure out how to complete these assignments.

google, stack overflow... etc...

I find myself doing other MOOCs, online tutorials and reading stack overflow for hours to find what I need to do the assignments.

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johns hopkins in 8 reviews

Thanks Johns Hopkins University!

Kudos to the Coursera and The Johns Hopkins Team for putting up this course together!

thank you Coursera and Johns Hopkins University.

Note: I'm enrolled in Johns Hopkins Data Science Signature track.

This course was as equally bad as the rest of the courses in the "Data Science" track through Johns Hopkins University.

My opinion of Johns Hopkins has lowered a lot after realizing they actually accept money for the Signature Track of this course--if this is the level of teaching Johns Hopkins attaches its name to, prospective students should stay away.

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Coursera

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Johns Hopkins University

Rating 4.1 based on 2,515 ratings
Length 5 weeks
Starts May 11 (13 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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