Data scientist at New York university | coursera | IBM


(popping) (upbeat music) – Everybody knows how to program, at least a little bit. They all have a little bit
of programming background at least, and some of them have a lot. Some of them are masters of
science and computer science, some of them are MBA
students who’ve come in from technical fields
and programmed every day. And others are ones who maybe took a programming course in
college four or five years ago but at least they can
think computationally, which I think is the most
important thing that they need. (music) Data science and business
analytics have become very hot subjects in the
last four or five years. We have new tools, we have new approaches, and we have lots and lots
of data that traditional techniques just couldn’t
really store and handle. I think the word is out. I think at this point, at first, companies and employers
understood the need, especially in certain fields. I can remember talking to a
major bank three years ago about big data and there was
one little group in the bank where one person had a little effort in putting a little cluster together. Now that same bank has five
or six major big data clusters and they’re putting all of
their credit card data in it and they’re grinding it
upside down and sideways, using all sorts of data
science kinds of techniques. Two years ago, or was
it last year, I think, our undergraduate dealing with data course had 28 students in it. This year it has 140. So that means that the parents are now beginning to get the word, because one thing we
understand with our undergrads is the parents who are
paying very hefty tuitions, they, you know, they tell
their sons and daughters, “You know, you should be
an accountant,” right? Or, “You should go into
financial services, “or into marketing, ’cause
this is where the money is.” Now, they’re getting the word that maybe you should take some more
STEM classes in high school and be ready to go into data science or go into fields where analytics has become more and more important. (music) It depends on who you are (laughs). I have my own definition of big data. My definition of big data
is data that is large enough and has enough volume and velocity that you cannot handle it with
traditional database systems. Some of our statisticians think big data is something you can’t
fit on a thumb drive. Big data, to me, was started by Google. When Google tried to
figure out how they were, when Larry Page and Sergey
Brin wanted to, basically, figure out how to solve
their page rank algorithm, there was nothing out there. They were trying to store all
of the web pages in the world, and there was no technology,
there was no way to do this, and so they went out and
developed this approach, which has now become,
Hadoop has copied it, but this is where all these large, big data clusters are found. But big data has now also expanded into, how do you analyze? There are new analytical techniques and statistical techniques
for handling these really, really, really large data sets. We’ll probably get to deep learning at some point along here. (upbeat music)

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