Most of the components of data science have been around for

many, many, many decades.

But they're all coming together now

with some new nuances I guess.

At the bottom of data science

you see probability and statistics.

You see algebra, linear algebra

you see programming

and you see databases.

They've all been here.

But what's happened now is we

now have the computational capabilities

to apply some new techniques - machine learning.

Where now we can take really large data sets

and instead of taking a sample

and trying to test some hypothesis

we can take really, really large data sets

and look for patterns.

And so back off one level from hypothesis testing

to finding patterns that maybe will generate hypotheses.

Now this can bother some very traditional statisticians

and gets them really annoyed sometimes

that you know you're supposed to have a hypothesis

that is not that is independent of the data

and then you test it.

So once some of these machine learning techniques started

we're really the only thing

the only way you can analyze

some of these really large

social media data sets.

So what we've seen is that the combination

of traditional areas computer science

probability, statistics, mathematics

all coming together in this thing that we call

Decision Sciences.

Our department at Stern

I'll give a little plug here

we happen to have been very well situated

among business schools

because we're one of the few business schools

that has a real statistics department

with real PhD level statisticians in it.

We have an operations management department

and an information systems department.

So we have a wide range of computer scientists

to statisticians, to operations researchers.

And so we were perfectly positioned

as a couple of other business schools were

to jump on this bandwagon and say; okay

this is Decision Sciences.

And Foster Provost who's in my department was

the first director of the NYU Center for Data Science.

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