- [Instructor] Elsewhere in this course,
I've introduced you to the three V's of big data.
Those are volume and velocity and variety.
And when you put those together, you have big data.
But you may be familiar with another three-part Venn diagram
this time serving as the tripartite definition
of data science that involves hacking skills
and math and statistics and substantive expertise
and that together constitutes data science.
And this model was created in 2013 by Drew Conway.
Now if you want to use shorter names for each of these,
you can call them code and quant and context.
By the way, most people have an intuitive feel
for why coding and quantitative skills
are important to data science,
but context or domain expertise
might need a little bit of explanation.
And I like this quote about why context expertise matters.
Organizations already have people who know their own data
better than mystical data scientists and this is a key.
The internal people already gained experience and ability
to model, research and analyze.
Learning Hadoop or any other big data platform or method
is easier than learning the company's business.
And that's by Svetlana Sicular of Gartner, Inc.
And so the idea here is it's easier to take somebody
who understands your business
and who understands the way things work within that domain
and get them the technical skills
than it is to do the other thing.
Now the data science Venn diagram
also labels the intersections of pairs of circles.
So for instance, here at the intersection of coding ability
and quantitative ability is machine learning
according to Drew Conway.
And then over here at the intersection of quant and context
is traditional research.
And then finally over here in code and context,
Drew Conway calls it the danger zone
and I have different things to say about that,
but the important to know about this
is that data science and big data
for many years were taken as practically synonymous terms.
I want to show you in some of the following videos
how the two relate but also the ways in which they diverge
and can represent different practices with different goals.