I guess if you want to enter a new field and you need to have some certifiable expertise, this may be a good option. That being said, if the field you plan on entering really does require some documented education, having this certificate will not even put you in the same playing field as those with actual degrees in the field, not to mention those with advanced degrees.
Personally, I'd prefer the less technology-specific topics already on offer. But, my employer would be much more likely to hire someone with UW's course-mix. So, there should be some demand for that.
And, if we are talking about a career decision, $3,000 is small potatoes compared to the value of getting the right topics.
The site appears to be push sponsor products and don't really talk about alternatives. Expect to see lots of endorsements of IBM products.
Of course, I wonder if this certificate gets me anywhere as far as employment goes.
Because this is the response of the "university system" trying to protect its cash flow. Perhaps you missed it, but udacity said recently it will do some certification program for "minimal costs", which was the beginning of the conversation. Education should be free (Khan style), says Udacity.
The University establishment's response is "here we will give you certificates, but you have to give us three grand."
Money for certificates, knowledge for free.
Are today's "data scientists" really just software devs who have specialized in digging around in data and using various data mining algorithms with only a superficial understanding of their inner workings?
For example I couldn't implement an SVM library from scratch to save my life, but I do understand what it means to be a 'maximum margin' classifier, from a high level how the 'kernel trick' works, and why you would tune regularization and cost parameters. However this knowledge has been enough to help me in quite a few interesting problems.
Reading accounts of how others have solved real world data mining issues it's amazing how often a very simple model will do the job, and also how often, even among more serious researches, there's a bit of intuition in finding the right combination of parameters, and lots of trial and error in searching for which model/blend of models really does the job.
I think there's a lot of room for more people approaching data mining with the 'hacker' mentality. Sure you don't want 'data scientists' using a randomForest whose eyes glaze over when you mention the word "ensemble", or someone who couldn't explain in plain terms what a "maximum margin hyperplane" is. But, there is a growing space for practitioners in this space, that aren't necessarily as strong in the theory as people working in the pure research space.
It seems like "ensemble" methods - combining the results of several different algorithms - is generally a less-than-rigorous exercise that involves throwing a bunch of different approaches at the problem and averaging the results.
It is good to hear that there is "a growing space for practitioners in this space, that aren't necessarily as strong in the theory." But the term "Data Scientist" seems a bit lofty for folks doing this sort of work.
Interesting side note for ensembles: 'averaging' is usually not one of the best methods for blending results. More successful approaches include using either a perceptron or a simply training a linear model to find appropriate weights for predictions from each individual model. I've even had a case where simply picking the MIN of each set of predictions worked surprisingly well for a particular problem.
The above btw is something that I think a "Data Scientist" should know, and is well out of the scope of a software engineer who just plugs values into prepackaged algorithms. A "data scientist" should be able to read papers  that explain these things, which is more than many software engineers do.
Now I'm not a data scientist, but while I can't write an SVM from scratch, when I'm working on data mining problems I am reading several academic papers a week. I really think we're looking at two sincerely distinct areas of expertise and it's not too lofty to look at someone who has to read academic papers to do his job as a "scientist".
Ideally, no. We're witnessing an overuse of the name "data scientist" (which has its own problems, but that's another story). There's a non-trivial difference between a data scientist who understands the theory used for the EM algorithm or belief propagation, and a "data scientist" who is performing large-scale data analysis using various data mining tools.
Unfortunately, they're both getting lumped together. To become one of the former, you need graduate-level maths, CS, and statistics, while this certificate caters to the latter.
There's a lot of non-PhD level work that goes into a large research project, often for aspects you may not have considered like animal care.
A "Data Scientist" might be a specialized software developer, but it doesn't follow that they have only a superficial understanding of the inner workings, even if that understanding is not good enough on its own to do much original research.
This is the first I've heard the term "Data Scientist," though Harvard recently announced a masters-level "Computational Science and Engineering" degree. (http://news.harvard.edu/gazette/story/2012/06/a-new-masters-...)
The bottom line is that due to the amount of data being generated by research, demand for programmers to help deal with it is rising.
I love this explanation on quora btw. http://www.quora.com/Career-Advice/How-do-I-become-a-data-sc...
It's funny, on the NYC subways the city has now put up ads warning kids against going to college, and telling them to call the hotline to ask if the college is credible before enrolling.
Is science only science if it is aimed towards publishable research (which is clearly the academic view)?
The lab techs that do scientific testing under the direction of a PI, they do science, right? Does that mean we need to relabel their badges to Scientist?
Don't take my word for it, though--you should visit us and find out what data scientists do!
8 hours lecture online per week + whatever offline work, for 4 semesters. That format, at minimum passes the "sniff test". I think four semesters at that rate is long enough to legitimately teach the content.
My only question now is how well received is it by the world at large? Has anyone hands on experience with ITB?
Just as a disclaimer: I have not relationship with ITB or RapidMiner. A while ago I played around with the software because some of the tutorials are really interesting and very accessible for someone like me who lacks a deep statistical understanding(i.e. http://www.youtube.com/watch?v=OXIKydgGbYk).
My personal advice is to ignore any future advantages that may or may not come as a result and focus on doing it because you simply want to do it. If you enjoyed the process and feel fulfilled at the end, it doesn't matter what may come as a result. If you need to work hard to sell yourself on the idea of doing it, it is probably not worth doing.