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Coursera – A View from the Inside

I have a unique opportunity coming up to impart an inside view of the Coursera MOOC platform. I’ve been asked to be a Community TA for the Computing for Data Analysis course that’s starting Monday, Sept. 23 and will run for 4 weeks. Here’s a video from Professor Roger Peng who’ll be teaching the course:

I took this class myself a while back to get a feel for the MOOC concept and found it to be an excellent experience. I especially remember the good help provided by the TA’s so I’m eager to give back by being a TA myself this time around. The course is about learning the fundamental computing skills necessary for effective data analysis. Students learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods. If you want to get your feet wet with R, this course is a great way to dive in the pool and have some fun.

Most Coursersa classes include a series of short videos (broken down into 8-10 minute segments) featuring in this case, Dr. Peng. You can watch the content at your own pace and as many times as you wish, although you’ll need to keep pace with the class in order to take the graded quizzes and complete the programming assignments on time.  Much of the online education experience comes from participation in the discussion forums where you can converse with fellow students and TAs to ask questions and get help with the assignments. I found the online format a great, low-stress way to learn.

You can sign up for this free class here. The last time it was offered around 70,000 students signed up. It should be quite a ride!

NOTE: this class is listed as Data Science 102 in my pseudo degree program.

Daniel – Managing Editor, insideBigData





  1. The Computing for Data Analysis class has completed and I must say my experience as Community TA was a very positive one. This was my first role as TA for a class at Coursera. It was an eye opening experience trying to assist a class with over 70,000 registered students.

    It was a tough, short class getting people up to speed with the R statistical language. Many had trouble with such a quick paced schedule; lectures, quizzes, programming assignments. Some were completely swamped and required special coaching and encouragement. One of the longest discussion threads involved commentaries about the value of the class, but these were from people who weren’t able to devote the proper amount of time learning the subject. In addition to the course work, you really must do independent research on various subjects outside of the class.

    As is always the case when teaching or TAing classes, I learned a lot myself. Some questions the students would ask I would never have thought to ask myself, so I had to do research in order to provide cogent responses.

    I would highly recommend this class, so if you want a course to give you the structure upon which you can learn R, then this is the one. It will be offered again in the near future:

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