What is data science and
what do data scientists do?
Stick around and you'll find out.
 
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So what is data science
and the role that Harvard Business Review
called the sexiest job
of the 21st century?
The word science in
the phrase data science
means using the scientific
method to turn data into value.
That means asking the right
questions, creating hypotheses,
and then devising experiments
to test these hypotheses.
Ultimately, these
experiments usually result
in conclusions,
discoveries and inventions.
And in the case of AI and machine learning,
in predictive and prescripted models.
In an article of mine,
I define what I call the pillars
of data science expertise.
And in an ideal world or
unicorn-like scenario,
these pillars represent the areas
that data scientists should be an expert.
They are business or domain expertise.
So think about an MBA type person
that's also an expert
in some industry or domain.
Mathematics expertise, so things like
statistics and probability.
Computer science expertise,
so think, you know, software
architecture and engineering
and communication expertise,
so things like written
and verbal communication,
so that data scientists can
deliver results and conclusions
and reports and findings to
senior executives and so on.
In reality, people are usually strong
in one or two of these pillars,
but not equally as strong in all four.
If you do find yourself a
data scientist that's equally
as strong in all four of these pillars,
then you've done a great
job and you found a unicorn.
So basically, based on these pillars,
a data scientist is somebody that's able
to extract meaningful
information and insights
from existing data sources,
as well as identify and
use new data sources
in order to help support
and drive business decisions
and actions that ultimately
achieve business goals.
And ultimately this is done
using business domain expertise,
along with effective communication,
and having the ability to use any
and all relevant programming languages,
software packages and libraries,
data infrastructure, and so on.
The process the data scientists
use to turn data into value
is pretty similar and
common across the board.
Although have different models
and names associated with them.
One of the more common
models is called Crisp-DM.
And of course, use the comments
below to let us know of
any other process models
that you've used or recommend.
In my book "AI for people in business,"
I introduce a model that I created called
the Gabdo process model.
And you know, I regularly
work with executives,
managers, entrepreneurs, and so on.
And so I created the
model just because I think
it's better suited to
that type of audience.
But there's absolutely nothing wrong
with the Crisp-DM model or
any other model like it.
The Gabdo process model
is a model that consists
of five iterative phases
which are goals, acquire,
build, deliver and optimize.
And the reason they're iterative is that
any one of these phases
can lead back to one
or more of the phases before it.
And a lot of that comes
from the experimental
and exploratory sort of
scientific nature of data science,
as we've already discussed.
All right, well, hopefully
this video helped
you understand data science
and what data scientists do
a little bit better.
If you like this video, be
sure to click the like button
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and I'll see you in the next video.
