Whenever you have a resource
that can, there are different
ways you can approach it.
Take the universe.
For example, you could either go the
astrophysics route and study the physical
properties of celestial bodies, or you
could go the cosmology road, which covers
the nature of the universe as a whole.
The interesting part is
that both are related.
You could have something similar with
big data and to use to study data
science or get into data analytics,
or maybe even both, but how do
you choose what's right for you.
Before we dive into that, don't
forget to subscribe to the
channel and hit the bell icon.
My name is Rebecca and this is data
science versus data analytics explained.
Let's get down to the basics.
Data science is a multi disciplinary
field of raw, unstructured data where
it comes from, what it represents and
the waste by which it can be transformed
into valuable input and resources to
create business and ID strategies.
In contrast, data analytics focuses on
processing and performing statistical
analysis on existing datasets.
Simply put, data science is focused
on finding the right question.
Do ask.
Well, data analytics finds
that answer to that question.
You does science use a statistical
methods, computer science, machine
learning and mathematics to pass
through big data to estimate the
unknown by asking questions, writing
algorithms, and building models.
Data analytics also makes use of
statistics, but in a much broader
sense, which help combine diverse
sources of data and locate meaningful
insights with simplifying the results.
So in this sense, data analytics, it
doesn't necessarily require an in depth
knowledge of the subject, but it can be
supplemented by learning the tools needed
to analyze data like Tableau and power BI.
So when you're sorting for jobs,
you have some useful pointers
to make your hunt Vizio data.
Scientists are required to have
heavy programming and coding
abilities, knowledge of machine
learning and software development.
This is needed since data scientists
need to be able to design and
build data models and algorithms.
Data analysts, on the other hand,
should have data mining odd, odd SAS,
SQL statistical analysis, database
management, and reporting to name a few.
As part of the skillset.
Data science are usually found
working in machine learning.
AI and corporate industry is trying
to establish solutions to problems
that haven't been thought of yet.
Data analysts usually work in sectors
like healthcare, gaming, travel, and such.
Industries with immediate data needs,
data analysts and data scientists have
job titles that are deceptively similar.
Given the many differences in
role responsibilities, educational
requirements, and career trajectory.
The two fields are fundamentally
two sides of the same coin.
By not understanding the two disciplines
of big data, it's imperative that we
perceive it not as data science was,
is data analytics, but as parts of a
whole that invited to the understanding,
not just the information that we
have, but how to better analyze it.
And review it.
I'm sure by now, whatever was
fogging up, your brain is long gone
and there's a new found interest
in some, or all of these topics.
But now what well for starters,
We have a bunch of tutorials on machine
learning, data science, artificial
intelligence, and so much more.
So go check that out on our channel page.
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Remember, the only learning
that matters is great learning
data scientists, data analysts.
They kind of sound the same, right?
Well, one day it takes him a bee
trick more than double the other.
Stick around to know who that is and
why they take home the big bucks.
Let's take an analogy to understand this.
It's not a perfect one, but it should
help consider a formula one racing team.
Now, there are some factors that are well
known to affect how car performs during
the race, and these need to be monitored
and relate to the driver and the team.
Chief.
Things like the tire wear, tire
pressure, fuel usage, and so on.
Analysis is done on these
factors to measure and tweak the
current performance of the car.
Now, this is similar to what a data
analyst does because a data analyst must
understand the data that the company
decides is important and analyze it.
Lends it and then provide a
visual representation of the
data to business decision makers.
Now going back to the formula one
analogy, there are also factors
beyond these well known ones
like tire pressure or fuel usage.
That can also affect the
performance of the car, right?
A huge amount of data is picked up
by the sensors in a formula one car.
There is also a lot more data that
is currently not being monitored.
Since it's influences unknown,
we don't know what it does.
Now imagine if someone was able to
decide which of the data that is
currently not being measured was
actually important and significantly
affected the ponds of the car.
Now imagine further that they could then
go ahead and create a model that reliably
explains and shows how this data could
help predict the performance of the car.
Now this is kind of.
What a data scientist is expected to
do because a data scientist is largely
looking at data that isn't currently
well understood by his or her company.
He or she has to figure out what data
is important, so they acquire that
data, they clean it, the extrapolate
from it and find missing values.
We then formulate a theory or hypothesis.
And tested using math statistics
and predictive modeling.
Once they verified their results,
data scientists then communicate
them to business stakeholders and
work with them to translate these
results into business action items.
So to put it simply, a data scientist for
the lack of a better term, is a rock star.
He or she has a strong business
Standing strong math and stats
skills, strong computer science and
machine learning skills and strong
communication and storytelling skills.
Now, realistically, hardly anyone is
equally strong at all these fronts, but
these are the ideal characteristics.
So to summarize some of the key
differences, data scientists
must have business knowledge
and good communications.
Skills.
Data analysts need not.
So ask yourself if you are interested
in the business side of things or not.
And if you are extroverted data
scientists are experts in machine
learning and building statistical models.
Data analysts do not need these skills.
So do you have or want to
build the skill sets needed.
