Hi Mark,

 

Just for everyone else's info, Machine Learning uses Octave. I took the
opportunity to learn a bit of Octave and got credit for the course. I
liked the lectures myself and learned a lot. It was paced well for
people with jobs and I also found the projects to be interesting and a
bit challenging. I worked hard on parallelizing all of my code - I
didn't use for loops unless absolutely necessary. Additionally, all the
grading was automated. An Octave script is used to manage the
submissions, and was pass/fail.

 

I understand that there were enrollees from every country except North
Korea. I wonder how people in emerging countries will use their new
knowledge?

 

I'm looking forward to the Computing for Data Analysis course, which
uses R. Another course, Data Analysis (using R as well), seems to build
on Peng's course.

 

https://www.coursera.org/#course/dataanalysis

 

I would say investigate, enroll, and un-enroll if you aren't interested.
That's the beauty of the system - it's free, and these aren't college
credits. If you choose to get the certificate, turn it over to your
employer.

 

And as a (hopefully) future university instructor, I found that this
experience opened my mind to online learning. Faculty could

*         Ask students to enroll online after the first day of on-campus
class (and provide evidence of enrollment).

*         Meet with the class twice a week for discussions, go over the
lectures, and help with learning Octave/R/etc.

*         Students provide the instructor with screenshots to show they
have completed the work, and submit their work for independent review by
the instructor.

*         To obtain full college credit, students then complete a
project of their own, using what they have learned in the course along
with Octave/R/etc.

Any college without the resources to add data analysis, computing with
data, machine learning, or other such courses should consider this as a
cost-effective way of expanding their offerings.

 

Thanks,

Dale Smith, Ph.D.

Senior Financial Quantitative Analyst

Risk & Compliance

Fiserv.

107 Technology Park

Norcross, GA 30092

Office: 678-375-5315

Mail: dale.smith@fiserv.com

www.fiserv.com <http://www.fiserv.com/> 

 

 

On Fri, Aug 24, 2012 at 7:43 AM, Smith, Dale <Dale.Smith@fiserv.com>
wrote:

I searched the third quarter archives for any mention of this, and
didn't find anything. So here goes. Eric Zivot is using R in the course.



https://www.coursera.org/#course/compfinance



I highly recommend Coursera. I took Machine Learning this past spring
and highly recommend it and other courses. Alas, I can't sign up as I'm
signed up for Computing for Data Analysis (another course using R).



https://www.coursera.org/#course/compdata



Thanks,

Dale Smith, Ph.D.

Senior Financial Quantitative Analyst

Risk & Compliance

Fiserv.

107 Technology Park

Norcross, GA 30092

Office: 678-375-5315

Mail: dale.smith@fiserv.com <mailto:dale.smith@fiserv.com>

www.fiserv.com <http://www.fiserv.com/>




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