Data Science Education Gets Personal

by Joseph B. Rickert

by Joseph B. Rickert

It is difficult to imagine that there is anyone on the planet with an internet connection and a desire to learn something new who has not at least looked into taking a massive open online course (MOOC). Last Fall, in an 11/4/12 article, the New York Time declared the Year of the MOOC and quoted one of Coursera’s founders, Andrew Ng, of Stanford Machine Learning fame, as boasting that with over 1.7 million students “Coursera was growing faster than Facebook.” This year with both Udacity and Harvard and MIT backed edX offering interesting and challenging courses the growth of MOOC enrollment must be astounding indeed.

Being the perpetual student, I have looked over the syllabi of quite a few courses that seem to be pretty exciting, but I am struggling to carve out the time to devote to them.  MOOC courses are “free”, but for a working professional they not without opportunity costs. In fact, it is not clear that a course designed to add value to 100,000 plus people at one time is the most effective way for professionals working in or around data science to make progress.

Tony Ojeda of the R-users-DC saw the interest professionals have in learning more about data science, and realized that even with the prevalence of MOOC courses there are educational needs that are not being met. In a recent email Tony wrote:

I started attending data meetups like R Users DC and Data Science DC about a year and a half ago … and these events really made me realize that there are a bunch of people out there who, like me, wanted to learn more about data science, machine learning, and analytics.  However, I noticed that there isn’t really a set path for people that want to get started learning data science.  It all depends on your current context and learning how to apply the methods and tools to what you’re currently doing.

Tony also noticed that many of the people attending the meetups were very knowledgeable about statistics, programming or other aspects of data science. And, as he describes it:

I’m sort of an efficiency freak, so I automatically thought, “It would be awesome if I could just find someone who knew more than me about whatever I’m trying to do so that I could get past these hurdles and get my work done.” 

The result of Tony’s musings was that on January 9^th of this year he announced www.sagebourse.com, a bare bones site for matching students with data science tutors in the greater DC area (including Maryland and Virginia). Students accessing the sight can express and interest in being tutored in either Data Science or Programming. Currently, Data Science topics are limited to Statistics, R, Machine Learning, Matlab/Octave or Excel.  The programming languages listed are: Python, Ruby, Java, Javascript, C, C++, Perl and PHP. In addition to these topics, sagebourse.com is also looking for tutors in SAS, SPSS, Stata, Hadoop, Access and Data Visualization.

Tutors who register with the sagebourse.com “bid” on the gig by providing the hourly rate at which they are willing to teach a subject. When students register with the site they fill out a form indicating the subject they want to be tutored in, provide contact information and indicate where they would want the tutoring to take place. Students then receive an email with bids from several tutors. Presently, the process of matching tutors with students takes a few days depending on the responsiveness of tutors and students.

These are still very early days for sagebourse.com. About 20 tutors have registered with the site and according to Tony they have already had a “handful” of successful tutoring sessions. Statistics and R are by far the most popular areas of expertise professed by the tutors who have registered, and “In terms of demand from students, it has been R, Statistics, and Machine Learning in that order.”  

I am excited about what sagebourse.com is trying to do, and I hope that it can make a big, positive contribution to the R community.