“Computing for Data Analysis” with R on coursera

Just stumbled on across a course on coursera titled “Computing for Data Analysis” taught by Roger D. Peng the Johns Hopkins Bloomberg School of Public Health.

Here is the description of the course.

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.

I just signed up for it! This course looks like a great opportunity to sharpen  skills in R and learn new things.

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10 thoughts on ““Computing for Data Analysis” with R on coursera

  1. Thanks. I signed up for the course. Sept is bit far though. I am trying to learn R now and so far it is challenging.

    • pie,

      Glad to hear you signed up. If you are just starting to learn R and struggling, my best advice is to be patient. When I first started using R I think I swore it off and quit at least three or four different times. You’ll be struggling along wondering if the light at the end of a tunnel is really a train and then all of a sudden a lightbulb will go off and you will be able to understand the syntax and the way things work in R.

      Ross

      • A presenter made a metaphor between R and smoking:

        At the start, you make feel headaches and gags but you will eventually have the pleasure and be addicted to it.

        Worth the pain and keep going mate.

  2. Just signed up myself. I’ve been following the pdf guide “Introduction to R” available on the CRAN website, as well as a few others, but I still struggle to see the big picture. Its like, “okay but so what, I can do that in excel…”

    Hopefully this course will clear away my doubts & frustrations.

    • Just stick with it and you’ll wonder why you ever used excel. Common things you do in excel with tabular data such as sorting, filtering, subsetting, summary statistics, etc. you can do with one or just a few lines in R. The visualization packages such as ggplot2 and googleVis put the excel charts to shame.

      Here is my list of top 5 go to resources when I get stuck:
      1. Stackoverflow
      2. Stackoverflow
      3. Stackoverflow (I hope I got the point across :) there are some real R wizards on there!)
      4. A little book of R for time series
      5. Quick-R

  3. Thanks. I think “introduction to R” on CRAN could have been written a bit more user friendly especially later sections. For me picking up a new language is generally not hard as I already has prior experience with C, Java, Python and C#. The primary challenge for me with R is getting idea of what stuff to read, where to find and in what order. My intent for learning R is primarily for checking out portfolio management and asset allocation/rotation strategies i.e., (a) do faber folio and see how it looks like with various money management algorithms and (b) write a portfolio management wrapper to read trade data from a file, create correlation graphs and later see how results would look like based on strategy diversification and various money management algorithms. I can’t find documentation on quantstrat. I posted in r-sig-finance and got a response on how to construct pdf file. I have to investigate that.

    • The most help I have found with quantstrat is exploring the demos that come with it. You’ll have to explore the folder that the quantsrat packages is downloaded into. quantstrat is a great framework for backtesting that is very flexible. The flexibility allows you to write custom functions and then have a signal based on crossovers, thresholds, etc.

  4. Andrew, I do feel though R is worth learning given its lightweight, modelling capabilities and support system (i.e., experienced R bloggers) around it.

  5. Hey Ross,

    Thanks for sharing the link to “Computing for Data Analysis” with R on coursera. I never heard of coursera before, but it looks mighty impressive! I’ve signed up to the course and now I’m really looking forward to it.

    Thanks again!
    GW

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