- [Instructor] You're familiar by now
with the 3 Vs of big data,
the Volume and Velocity and Variety of the data.
And you're also familiar with data science,
which involves Coding and Quantitative ability and Context.
Now, just as I have shown you that you can do data science
without needing big data, you can actually do big data
without needing the full data science toolkit,
and you can still get valuable and actionable insights
from those analyses.
So let's take a look at, for instance, when you would use
the coding and the quantitative abilities of data science
without necessarily needing the context
or the domain expertise.
The best example in this case is machine learning
or specifically, deep learning neural networks.
In this case, you can create a black box,
which means you don't necessarily know
exactly how the algorithm is processing the data,
but you can still get very useful categorizations
and identifications of cases that fit,
cases that don't fit, of anomalies,
and you can find how you can best serve your clients
even without having the strict domain expertise
that's normally involved with that.
On the other hand, there are projects that involve
context expertise and quantitative ability
without necessarily requiring the ability to code.
Now, in Drew Conway's Data Science Venn Diagram,
he calls this traditional research.
Now, a person with traditional research training,
as is common in my own academic field,
is probably going to be very, very good at their context,
they'll be experts on it,
and they're probably sufficiently skilled
with quantitative abilities to get their work done.
Chances are, they're not prepared to deal with
the volume, velocity or variety of big data on their own.
So in this case, those who are best skilled
at traditional research may not actually be able to engage
in a big data project the way it's traditionally defined.
And then, finally, there's what Drew Conway calls
the danger zone.
He says that's when you have the context
or the content expertise and the ability to code,
without having quantitative ability.
Now, I can think of lots of situations
where that's not going to cause problems.
For instance, in the big data world,
word counts, where you take large amounts of text,
either from individual documents, like this,
or from a collection of social media posts,
and counting how often the words happen.
There's nothing statistically sophisticated about that,
but it can be a great way of processing data,
getting an overall feel for what's happening
in the document and seeing how things change over time.
These word counts involve, again,
no statistical information except for saying how common
this happens and this happens.
But they are absolutely big data tasks.
Text analysis, especially natural language processing,
is one of the foundational tasks of data science,
even if it doesn't involve specific quantitative,
mathematical or statistical tasks.
And so, the point of this is,
you can do big data projects without necessarily having
to have all of the elements of the data science toolkit,
and lets you know they are separate disciplines,
even if they traditionally frequently overlap.