Data Science Education gets personal

March 14, 2013

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

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 enrolment must
be astounding indeed. I think this is all good and I have written about it
before .

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

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 9th
of this year he announced, 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, is also looking for tutors in SAS, SPSS, Stata, Hadoop,
Access and Data Visualization.

Tutors who register with the “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 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 is trying to do, and
I hope that it can make a big, positive contribution to the R community.

To leave a comment for the author, please follow the link and comment on their blog: Revolutions. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

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