2013 September DSMD Event: Teaching Data Science to the Masses

Stats-vs-data-science For Data Science MD's Septmeber meetup, we were very fortunate to have the very talented and very passionate Dr. Jeff Leek speak about his experiences teaching Data Science through the online learning platform Coursera. This was also a unique event for DSMD itself because it was the first meetup that only featured one speaker. Having one speaker speak for a whole hour can be a disaster if the speaker is unable to keep the attention of those in the audience. However, Dr. Leek is a very dynamic and engaging speaker and had no problem keeping the attention of everyone in the room, including a couple of middle school students.

For those of you who are not familiar with Dr. Leek, he is a biostatistician at Johns Hopkins University as well as a instructor in the JHU biostatistics program. His biostatistics work typically entails analyzing human genome sequenced data to provide insights to doctors and patients in the form of raw data and advanced visualizations. However, when he is not revolutionizing the medical world or teaching the great biostatisticians of tomorrow at JHU, you may look for him teaching his course on Coursera, or providing new content to his blog, Simply Statistics.

Now, on to the talk. Johns Hopkins and specifically Dr. Leek got involved in teaching a Coursera course because they have constantly been looking at ways to improve learning for their students. They had been "flipping the classroom" by taking lectures and posting them to YouTube so that students could review the lecture material before class and then use the classroom time to dig deeper into specific topics. Because online videos are such a vital component of Massive Open Online Classes (MOOCs), it is no surprise that they took the next leap.

But just in case you think that JHU and Dr. Leek are new to this whole data science "thing," check out their Kaggle team's results for the Heritage Health Prize.

jhu-kaggle-team

Even though their team fell a few places when run on the private data, they still had a very impressive showing considering there were 1358 teams that entered and over 20,000 entries. But what exactly does data science mean to Dr. Leek? Check out his expanded components of data science chart, that differs from similar charts of other data scientists by showing the root disciplines of each component too.

expanded-fusion-of-data-science

But what does the course look like?

course-setup

He covers topics such as type of analyses, how to organize a data analysis, data munging as well as others like:

concepts-machine-learning

statistics-concept

One of the interesting things to note though is that he also shows examples of poor data analysis attempts. There is a core problem with the statistics example from above (pointed out by high school students). Below is an example of another:

concepts-confounding

And this course, in addition to two other courses, Computing for Data Analysis and Mathematical Biostatistics Bootcamp taught by other JHU faculty, have had a very positive response.

jhu-course-enrollment

But how do you teach that many people effectively? That is where the power of Coursera comes in; JHU could have chosen other providers like edX or Udacity but decided to go with Coursera. The videos make it easy to convey knowledge and message boards provide a mechanism to ask questions. Dr. Leek even had students answering questions for other students so that all he had to do was validate the response. But he also pointed out that his class' message board was just like all other message boards and followed 1/98/1 rule where 1% of people respond in a mean way and are unhelpful, 1% of people are very nice and very helpful and the other 98% don't care and don't really respond.

One of the most unique aspects of Coursera is that it helps to scale to tens of thousands of students by using peer/student grading. Each person grades 4 different assignments so that everyone is submitting one answer and grading 4 others. The final score for each student is the median of the four scores from the other students. The rubric used in Dr. Leek's class is below.

grading-rubric

The result of this grading policy, based on Dr. Leek's analysis is that good students received good grades, poor students received poor grades and middle students' grades fluctuated a fair amount. So it seems like the policy works mostly, but there is still room for improvement.

But why does Johns Hopkins and Dr. Leek even support this model of learning? They do have full time jobs that involve teaching after all. Well, besides being huge supporters of open source technology and open learning, they also see many other reasons for supporting this paradigm.

partial-motivation

Check out the video for the many other reasons why JHU further supports this paradigm. And, while you are at it, see if you can figure out if the x and y axes are related in some way. This was our data science/statistics problem for the evening. The answer can also be found in the video.

stat-challenge

http://www.youtube.com/playlist?list=PLgqwinaq-u-NNCAp3nGLf93dh44TVJ0vZ

We also got a sneak peek at a new tool/component that integrates directly into R - swirl. Look for a meetup or blog post about this tool in the future.

swirl

Our next meetup is on October 9th at Advertising.com in Baltimore beginning at 6:30PM. We will have Don Miner speak about using Hadoop for Data Science. If you can make it, come out and join us.