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Johns Hopkins University

Computing for Data Analysis

Johns Hopkins University via Coursera

This course may be unavailable.


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, discuss 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, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.


A student who has completed this course is able to:

  • Read formatted data into R
  • Subset, remove missing values from, and clean tabular data
  • Write custom functions in R to implement new functionality and making use of control structures such as loops and conditionals
  • Use the R code debugger to identify problems in R functions
  • Make a scatterplot/boxplot/histogram/image plot and modify a plot with custom annotations
  • Define a new data class in R and write methods for that class

Taught by

Roger D. Peng


3.4 rating, based on 27 Class Central reviews

Start your review of Computing for Data Analysis

  • Life is Study
    Computing for Data Analysis is an introduction to the R programming language, but not an introduction to programming. This course is designed for people who already know how to program. The course description makes it seem like the class is intended…
  • Brian Borchers
    The lectures in this course were presented as online videos of up to about 1/2 hour length. The videos consisted of prepared slides with the lecturers voice in the soundtrack. To keep the videos from becoming too boring, there were multiple choice…
  • Anonymous
    A nice 4 week mini-course that thought me how to write R scripts. The video lectures are sometimes academic in the bad sense of the word (showing great learning, but not really that relevant). The weekly quiz questions were very functional. The real meat of this course were both practical assignments. I learned a lot from doing these (WARNING: you are given a real data set and real task to perform using these sets, but I had to use Google to find ways to do the task - very few hints and examples were given in the lectures -) After finishing the assignments I had acquired the skill to write R scripts. For me the course worked well, but I would not recommend it to those who are looking for an easy intro to R.
  • Anonymous
    The course is an introduction to programming in R, and as that it is quite good. It doesn't deal with many particularly difficult statistical concepts, but when he does he sticks to the name of the course and teaches only how to program it (e.g. on the lecture on simulation). I thought the pace was quite good, but some people in the forums complained it was too fast for them, so be warned (and as someone mentioned, be prepared to google some, but you should do that always: google is the bread-and-butter of any programer).
  • Santosh Goteti
    This course is mainly about R programming. If you are not from programming background, this course might be significantly tougher.
  • This course had severe flaws, I don't know if anything changed meanwhile. You shouldn't make onboarding unneccessarily hard for people who are interested in data analysis by starting with describing data strucures. That may or may not make sense in a computer science course, but not if people want to analyze data. So, those who are familiar with programming don't need such an introduction. On the other hand, those who are not familar with programming will probably be overburdened -- that's what actually happened as you could read in the discussion forum.

    Just dull presenting and showing ugly power point slides is no fun, the quizzes were meant to replicate facts from the videos, etc.
  • Anonymous
    Expected much more. This is not an introductory course to R. It requires you to have previous knowledge and experience in programming. Not all necessary knowledge to complete programming assignments is covered in video lectures. It's necessary to read and study from other sources. 3-5 weekly hours is not enough, be prepared to invest much more time trying to find our how to do things by yourself. 4 weeks is not enough to acquire proper knowledge of R. Basic topics are covered and some examples are shown, but you are not taught how to do things the R way. That is for you to find out.
  • Mark Tuttle
    Enrolled in previous offering but didn't have the time to participate. Am early on in the videos this time, and am impressed. This is a challenging subject to simplify and I think Peng is doing a great job so far. His lectures are easily more productive than using the pile of R books I have. On the one hand, Peng has a feeling for programming language design, and on the other hand his presentation is driven by anticipated "data" needs.
  • Anonymous
    I wish the lectures were more informative and helpful in answering at least homework questions. I found myslf learning from google eventually. I started Data Analysis by Jeff Leek, and like it much better so far. Everything is well explained, relevant exercises, there practical examples of real life applications.

    Hopefully, it will continue to be good!
  • Anonymous
    I took the course a couple of months ago and have been working with R a lot since. Peng did an incredible job - making a very complicated subject understandable, with excellent lectures and excellent assignments.

    I highly recommend this course.
  • Anonymous
    I just finished the course. The whole experience was great. The programming tasks were fairly non-trivial. The knowledge you would gain is absolutely worth spending an few hours a week on the course for four weeks!

  • Anonymous
    Good and fast introduction into the R package.

    A little bit too much ex-catetra, more small interactive exercises and some real world problems can make ths course more exciting.
  • Anonymous
    This series of courses is a joke! Don't waste your time!

    If you know how to use R for data analysis you don't need it - if you don't, you won't learn here!!
  • Anonymous
    Not the best course I took. All in all it reduces to a kind of introduction to R... "Computing for Data Analysis" is a bit of a mouthful.
  • Rafael Prados
  • Rajesh Kohli
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    Pablo Torre

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