Data has always been Centric
to any decision making. Today's
world runs completely on data
and none of today's
organizations would survive a
day without bytes and megabytes.
There are several roles
in the industry today
that deals with data
and most people have several
misconceptions about them.
I am Aayushi
from Edureka and let
me welcome you to this video
on the key differences
between three of the leading
roles in data management,
that are data analyst, data
engineer and data scientist.
So let's move on and see
what all we going to cover
in this session first
and foremost will be starting
by getting a quick introduction
about the roles as in who is
a data analyst, data engineer
and a data scientist,
then we'll be going
through the various skill sets
that these professionals
possess will also be looking
at various roles
and responsibilities.
And finally,
I'll conclude the session
by telling you guys this is Leo
what a data analyst
a data engineer
and a data scientist learn
so let's begin the session and
start with the very first topic
who is a data analyst.
Well a data analyst is the one
who analyzed all the numeric
and other kinds of data
and translate it
into the English language
so that everyone can understand
now this data is used
by the upper management to make
informed business decisions.
Now the main responsibilities
of a data analyst include data
collection correlation analysis
and Reporting next
is data engineer.
So a data engineer is the one
who is involved
in preparing data
for analytics calor
operational users.
So these are the ones
who develops constructs test
and maintain the complete
architecture of the large
scale processing system.
Now a typical data ingenious,
they include building
data pipelines to put
all the information together
from different sources.
They then integrated
Consolidated for the clean
and structure it
for more analytic 6.
So this probably varies from
organization to organization.
Next is a data scientist.
A data scientist is a one
who analyze and interpret
complex Digital Data
for instance statistics
of a website.
Now a data scientist
is a professional
who deals with your large amount
of structured as well
as unstructured data.
They use their skills
in statistics programming
machine learning in order
to create strategic plans
now data scientist
and data engineer job roles
are quite similar
but a data scientist is the one
who has the upper hand or all
the data editor activities
when it comes
to business related
decision-making data scientist
have the higher proficiency.
Now, let's look at the road map
which correlate these three
job roles to start off
with most entry level
professionals interested
in getting into Data related
jobs start off as data analyst.
So qualifying for this role
is as simple as it gets.
All you need is
a bachelor's degree
and good statistical knowledge.
Well strong technical
skills would be a plus
and can give you an edge
over most other applicants other
than this companies expect you
to understand data handling
modeling and Reporting.
Along with the strong
understanding of the business
moving forward the transition
between a data analyst role
and a data engineer one is
possible in multiple ways.
You can either acquire
a master's degree
in a related field
or gather amount of experience
as a data analyst adding
onto the skills of data analyst
a data engineer needs to have
a strong technical background
with the ability to create
an integrated API also need
to understand data pipelining
and performance optimization.
The next milestone
in data Engineers Courier
is becoming a data scientist
while there are
several ways in which
a data engineer can transition
into a data scientist rule
the most seamless one is
by acquiring enough experience
and learning the
necessary skills.
Now these skills include
Advanced statistical analysis
a complete understanding
of machine learning
and predictive algorithms
and data conditioning next.
Let us compare these different
roles on the basis
of their skills their roles
and responsibilities
in their day-to-day life
and finally discuss
the salary perspective first.
Let us see what are
the different skill sets
required for data.
Less data engineer
and data scientists.
So as discussed a data analyst
primary skill sets revolves
around data equation handling
and processing now
an ideal skill set
for this profile would include
data warehousing Adobe
and Google analytics.
Then you must have
programming knowledge scripting
and statistical skills reporting
and data visualization using
various tools database knowledge
like SQL or anything
and spreadsheet knowledge.
Well a beginner's
level programming experience
would also Aid
in building better
statistical models as well.
Now a data engineer
on the other hand requires
intermediate level understanding
of programming to build
our algorithms along
with a Mastery of statistics
and math most companies hiring
for data Engineers.
Look for skills,
like data warehousing and ETL
or you can say extract
transform load then it has some
Advanced programming knowledge.
Also Hadoop based analytics
plays a vital role then they
must have in-depth knowledge
of databases data architecture
and various machine learning
concept or you can say
algorithms knowledge fine.
Any a data scientist
needs to be master
of both the world's data starts
and math along with in-depth
programming knowledge
of machine learning
and deep learning.
Well the job description
for an ideal data scientist
include statistical
and analytical skills.
Then you have
various data mining
activities machine learning
and deep learning principles,
or you can also add up
to its various algorithms.
Then a data scientist
should also have in-depth
programming knowledge or you
can see such as in SAS are
or python languages now
that you have
a complete understanding of
what skill sets.
You need to become
a data analyst
a data engineer or a scientist.
Let's look at what
are the typical roles
and responsibilities of these
professionals now the roles
and responsibilities
of a data analyst data engineer
and the data scientists
are quite similar
as you can see
from the slides now a typical
data analyst is responsible
for statistical analysis
and data interpretation.
They should also
be well familiarized
with various data reporting
and visualization tools.
For example, if I
working on python,
you should know
the various python libraries
like matplotlib see zbornak.
Job, and similarly.
If you are familiar
with our language,
then you should go for ggplot or
any other visualization library.
Then a data analyst should
never compromise on the quality.
This should also be
very friendly with data.
It works for example
data equation maintenance
pattern detection data cleaning
and things like that.
Next comes to data engineer
well adding onto the work
of data analyst a data engineer
also maintains the architecture
the development of it
and testing of
that architecture.
So it basically involves
developing data sets using
machine learning techniques,
or you can say a data engineer
should also know
how to deploy
these machine learning
and deep learning models
and all the other tasks
assigned with them.
So for example,
predictive modeling searching
for hidden patterns
and similar tasks,
then comes your data scientist.
Now a data scientist
on the other hand
is responsible for a lot
of tasks is responsible
for mining of data then
develop operational models.
Then a data scientist
should also be explored
in machine learning
and deep learning techniques.
You should also be scale
in data enhancement
and sourcing method
These another important aspect
of being a data scientist
strategy planning
and data integration.
Now a lesser-known task
of a data scientist is impulsive
or you can say
or ad hoc analysis
and finally a data scientist
must be skilled
at anomaly detection
and performance tracking now
after these two
interested topics.
Let's now look at
how much you can earn
by getting into a career
in data analytics data
engineering or data science.
Now as you can see
the typical salary
of a data analyst is just
under fifty nine thousand
dollars per year there
as a data engineer can earn
up to ninety thousand eight
hundred and thirty nine
dollars per year.
Whereas a data scientist
can earn up to ninety
one thousand four hundred
seventy dollars per year.
So isn't this amazing guys now
looking at these figures
of a data engineer
and a data scientist,
you might not see
much difference at first
but delving deeper
into the numbers
a data scientist can earn
twenty to thirty percent more
than an average data engineer.
Also, it's been proven
by various job
posting from companies
like Facebook IBM That
basically coat salaries up to
one thirty six thousand dollars
per year now taking
this into consideration.
We also have an expert created
data science master's program
where you can find
all the necessary details
to become a radar scientist.
It include 12 courses
were 250 Plus hours
of Interactive Learning
along with the Capstone project.
You can find out all
the details curriculum
that timings everything
over here and let me also tell
you one more thing guys.
You will also be awarded
with an industry-recognized
certificate in the end.
So do check out this page guys.
I will drop the link
in the description box below.
Well, that's all for today.
I hope you guys
like this session have
a lovely weekend.
Enjoy.
Bye.
Thank you.
I hope you have enjoyed
listening to this video.
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Happy learning.
