- [Instructor] Elsewhere, I introduced you
to the three-part definition of data science
that revolves on coding
and quantitative abilities and context.
And while data science
has been strongly associated with big data,
it's still possible to do data science projects
without involving all of the elements of big data.
So, for example, you can use data science
on projects that have volume without velocity or variety,
so for instance, a very large and static data set
with a consistent format.
Genetic data's going to fall into that category,
or maybe something in data mining or predictive analytics.
You may be trying to predict a single outcome
like whether a person will click on an ad on a webpage.
In this case, the data set might have thousands of variables
and billions of cases,
but all of it's in a consistent format.
The size is a challenge,
but otherwise, it's a relatively,
at least in theory, straightforward analysis.
You can also have velocity without volume or variety.
So, think about streaming data
that has a consistent structure.
That might be looking at the number of hits on webpages
or searches for a particular term,
like searches for big data or data science
in the last decade on Google Trends.
You can also get data from a sensor
like temperature readings
that can come in hundreds of times per second.
So, this is data stream mining or sometimes called
the real-time classification of streaming sensor data.
So, you have to do data science
to be able to process information
that's coming in this fast and adding on to it,
even if it doesn't ultimately constitute
a large volume or if it has a very consistent structure.
And then finally, there's variety in the data
without necessarily having velocity or volume.
So, think about for instance,
facial recognition in a personal photograph collection.
You might say, maybe you have a few thousand photos.
That doesn't exactly constitute big data,
but because it's photographs,
it does have a lot of variety in the data.
But an even better example
is a data visualization of a complex, but static data set.
That involves the data science tool set,
even though it's distinct from big data
because it's characterized only by one of the three V's.
And so, data science and big data
do have a lot of history in common,
and a lot of practices in common,
but you can see from this
that you can do data science,
even when you don't have all of the elements of big data.