hi guys and welcome to this session by
Intellipaat
so according to Harvard Business Review
data science jobs are the sexiest job of
the 21st century
they have labeled it as sexiest job and
rightly so because these are the people
these are the professionals who are
highly paid can have a lot of perks in
their jobs they have a rapid career
growth and these are the people who are
most sought after in the 21st century
and they have high demand in their jobs
and they are working on the most
trending technologies in the world right
now so keeping all of that in mind we
have come up with this live session to
make sure that you understand what are
the roles and responsibilities of a data
scientists and what our data science and
all of that will be clearing that in
this session but before we begin with
our session please subscribe to our
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from Intellipat now let's talk about
today's agenda so today we will begin
our session with a very basic
introduction to data science where I
will try to explain what basically data
science is and what we are dealing with
and different technologies that comes
along with it after that we'll move on
to understand what who is a data
scientist so in that we'll be looking at
what are the basic skills which is
required to be a data scientist and what
are the basic requirements that goes
into becoming a data scientist after
that we will move on and look at the job
descriptions and we have clarified and
been through a lot of data and found out
the roles and responsibilities which are
there for a lot of data scientist jobs
out there and we have we have put it
down to three or four jobs there their
roles and we have combined their
responsibilities into one place and
there will be understanding the roles
and responsibilities of a data scientist
after that we'll move on to understand
what are the skills required to be that
data scientist who has all those roles
and responsibilities so different skills
that you will be needing to be ahead of
the curve and become the best data
scientist in the world after that we
will talk about the different salaries
of a data scientist where we'll be
discussing about a junior developer as
well as a senior professional who has a
little bit of experience in data science
how their salaries differ across the
globe
so we will be discussing that there and
finally we go on to understand how you
can go about learning
data science in the further learning
section of our agenda and we'll discuss
different ways you can learn about data
science right so let's begin with this
session so the first question that comes
to mind is what actually is data science
now this is something which many people
term as an umbrella term because it is a
multidisciplinary a field so if you go
according to the definition data science
is a multidisciplinary field that uses
scientific methods processes algorithms
and systems to extract knowledge and
insights from structure as well as
unstructured data so what does this mean
so what it basically means is that
suppose you are a company every startup
today is generating a huge amount of
data with every click and every page you
visit everything that you say there is
data generated right so everything that
you like or don't like is a data so what
the actually the job of a data scientist
is is to gather those data turn that
into a readable format or a format that
you can use model that data from that
data they need to extract the relevant
data that they need according to their
business requirements and then apply
some mathematical models and statistical
models what is that data and find out
the insights which with which you can
predict the future of your company or
the future of a product and look at the
past and how your product is performing
and how changing few methodologies can
make your product perform better
that is what big data science is all
about it is about gathering data making
it useful and extracting the data to
produce actual insights and to predict
the future that is what data science is
all about so what is this
multidisciplinary fees that we are
talking about so what multidisciplinary
basically means is you are using various
different fields which come together
combined together and all of their
efforts come together and give out the
final output which is the end results of
the data sign that is the inside that
you are getting so what basically goes
into all of this is the statistics so
you have a lot of data so what you do is
you get a lot of data you find different
methodologies to extract Dores data you
can use x SQL or you can use Excel in
different ways you have data you combine
all of that
data then you format that data into a
much usable form because some of the
data is very much unusable so you have
to cleanse that data scrub that data to
have the actual data which you need then
you apply your mathematical and
statistical models on those data based
on the requirements of your company so
if you are trying to solve a problem
there will be a specific approach to
have solved to solve that problem right
if there's a particular problem there
are few constraints which goes about
much better than the other so you need
to find a model which works best for
given best for your given data they
apply that statistical model then apply
machine learning and other stuff to find
out the insights about those data and
finally what you have to do is whatever
that you have extracted it's not
everyone in your company all the
stakeholders are not an actual
programmer so they won't understand what
you have done with the mathematical
model right so you need to make those
data into readable presentable formats
that what that is what you do through
data visualization tools you use tableau
or something like that to give an end
product to your stakeholders so that
they understand what you have done with
the data right so these are the
different fields which goes into the
data science statistics data analysis
machine learning AI deep learning data
visualization all of these combined
together work as a data science right so
what is the lifecycle for data science
so as I've told you before that it is
all about data right it's everything
that you have to do with the data so
what you start off is with the data
discovery where you find out different
sources of data and you try to capture
as much data as you can in the form of
structure as well as unstructured data
now structured data is somewhere you
have alignment on data and unstructured
format is random data which is which has
like no relation with it whatsoever
like one data point can be very
different from the other one so if what
you have to do is you have to gather all
of the data first in order to analyze
data so data discovery is the first
point in this life cycle of data science
then what you do is you prepare that
data so data preparation comes next so
what you have to do is you have to put
that unstructured and structured data
into a format which is common which can
be used for
you do applying statistical models and
mathematical models and machine learning
models so you have to do that so data
preparation is the next phase of data
science data science life cycle after
that once you have the data ready and
prepared and you have no false data and
random data in there what you do is you
device a mathematical model based on
your problem so if you are just trying
to find out like if a particular product
works for you in the future or not so
what you have to do is you have to
create a model for creating that model
you need to understand what are the
variables and constraint that go about
in predicting the maximum utility of
that resource right so if let's say
just the if we have data as names now
names won't affect how your product work
you need different data coming in there
to understand how your product works in
the market right so you won't be taking
all of the names into the account you
will be taking their age group or you'll
be taking if they like a particular
product and if your product is similar
to that yes or no so that mathematical
model you have to create those variables
which will come in hand where you want
to analyze your data so you have to do
mathematical modeling on that moving
ahead you have to get things into action
so once you have that determined those
mathematical models and machine learning
modules and you have understood how your
data is working you need to apply that
on your data and get the results out so
once you apply your mathematical models
on the data that you have based on the
business requirements which your
stakeholders have given you and once you
have solved the problem you have the
final data that you need right but not
all your stakeholders will be experts in
mathematics or computer science and they
will not understand the data so the next
step and the final step which comes
about in data science life cycle is
visualization that data and that is
where communication comes into hand so
communicating your findings
whatever the mathematical model dictates
or whatever the outcome that you have
you have to communicate that to your
stakeholders and that will be done
through mathematical modeling sorry that
will be done through data visualization
and that database realization could be
charts or it could be graphs or it could
be anything else based on the
tools that you are using or whatever the
data you have so communicating that to
your stakeholders is final part
in the lifecycle of a data science so so
who is a data scientist now we have
gathered some data from here in there
and we have put up a list of things
which are common in a data scientist and
that is what we have below so a data
scientist can be thought of as a part
mathematician part computer scientist
and part transporter so why do we say
trendspotter not transporter is a very
vital term in here because most of the
data that you have you are looking for a
pattern in everywhere since it is all
Maths so you are using maths everywhere
and you are applying artificial
intelligence to it you are applying deep
learning and different machine learning
models to understand what is the pattern
to predict what the future might be so
spotting that trend of what is the
product that is working right now in our
current field scenario is a very
important part of it you can be a very
good mathematician you can be a very
good computer scientist but
understanding the trends is very
important to solve any business problem
in the world and because they both
straddle business as well as I D words
they are highly sought-after and
well-paid so this is good this goes
without saying as I mentioned this is
the most trending job in the 21st
century so they are the ones who are
understanding the data which you have
they are they are the ones who
understand the computer science and they
are the ones who understand the data
behind and what they do is they use that
data to enhance their business by
applying mathematical models which suit
their business requirements so they
handled both the business as well as the
IT world and hence and they will be the
most sought after because they are the
ones who are taking some random raw data
which they have they're converting it
into something very meaningful and
something which can be used as a
business strategy to enhance your
business and people like this will
always be paid because they are helping
you grow your business right so when you
look at a data scientist there are few
simple skills which are prevalent in
everyone so these people are extremely
good at mathematics because they will be
performing computations which involves
different kinds of data different kinds
of Computing's will be involved
different kinds of mathematical and
statistical models will be used so these
people have to be really good at
mathematics second thing which is very
common among all of the data scientist
is they all have a very strong
analytical skill so if you give them
some data they will be able to analyze
it very easily what the data is about
and what that data tells you because the
raw data may have a lot of information
but understanding what the data is
telling you and giving that insight is a
very key factor when it comes to
becoming a data scientist so all of the
data scientists that you see have a very
strong analytical skill after that then
you have have all the data all that
analytical skill you have and you have
found out ok this is happening what you
need to do is to communicate that so
having a communication skill is very
very key if you are hoping to become a
data scientist because you will need to
explain to your variety of stakeholders
what your data actually tells you and as
I mentioned before not all of your
stakeholders will be computer science
experts on mathematical gods right so
you need to explain what you have in the
layman terms to those people so that
they can come up with their business
strategy and know about what your data
is telling right so I have mentioned
here some very common terms which is
common among the data scientists so if
you go around the world it you will hear
data scientists saying yo I need to this
visualize data so that is data
visualization they'll be applying
machine learning as well as deep
learning models to the data
sounds very tough but this is something
we have mentioned as a layman's term so
if you go about understanding machine
learning it's very interesting how they
analyze data using simple maths and then
there is pattern recognition as I
mentioned people need to spot the 10
trends of what people are referring to
right now and based on that they assign
some values to it so that their
mathematical model is actually accurate
in predicting what the future will be
and why you should choose your career in
data science is the next one so if you
are thinking of becoming a data
scientist tomorrow let's say you want to
become a data scientist even some ask you
why do you want to become a data
scientist so you tell them very simple
facts first of all it is the most
trending technology
21st century so it was highly in demand
everyone is looking for a data scientist
who is very competent who has a good
communication skill who has good
mathematical and analytical skills as
well secondly there is since it is the
most trending technology you have a lot
of job abundance because people in
across the startups like if you look at
any good companies fortune 500 companies
or even startups they're looking for
people who are good at data science so
that they can understand how their
company is doing and what they can do
better while launching a product or how
they can do better in their service that
they have actually right now and that
could be done only through if
understanding the data that you have so
there is a lot of job abundance in the
21st century for a data scientist next
since this is the most trending
technologies these people are highly
paid because of the skills what that
they possess is like compared to neck no
one like data scientist are the next
biggest thing in the 21st century when
it comes to jobs now let's talk about
the matter that we have come here on
this life to talk about which is the
roles and responsibilities of a data
scientist now we've looked across a
various job descriptions and found out
there are different kinds of roles able
roles available in the different
companies for a data scientist so we
have narrowed it down to four and these
are the most sort of the jobs in the
world right now these are the most
abundant jobs if you go about searching
for data scientists jobs in indeed or
Glass door or anywhere these are the jobs
which are the most prevalent in the
world right now
so broadly we have classified it under
four the first one is a data analyst
second one is a data engineer third one
is a machine learning engineer and
fourth one we have generalized this as
data science generalist
now a data scientist so I've mentioned
it here because some people might say
technically data analyst is a part of
data science but it's not truly data
science but when you go about reading
the job descriptions of many company
there are some companies where being a
data scientist is synonymous with being
a data analyst so here your might your
job might consist of tasks like pulling
out
from the SQL database or becoming an SQL
or tableau master and producing a basic
data visualizations and reporting
dashboards you may also be on occasion
asked to analyze the results of and test
that your companies are doing or some
date or visualize some data or make some
insights on the Google Analytics that
your company has so so once you have a
handle your day-to-day responsibilities
a company like this who are looking for
data analyst jobs environment to try new
things to expand scale say expand your
skill set is huge so you there's a sky
sky is the limit when you are looking at
a job as a data analyst second one is a
data engineer so some companies get to a
point where they have a lot of traffic
and increasingly huge amounts of data
right and they're looking for someone to
set up a lot of data infrastructure that
the company will need moving forward
they're also looking for someone to
provide analysis so you will see a lot
of job postings under the name both data
scientist as well as the data engineer
for this kind of position for those
people who are generating a huge amount
of data they need people like that for
that so so the basically what you will
be doing here is your job will involve
heavy statistics and machine learning
expertise and you will also need strong
software engineering skills so if you
are looking to become a data engineer
you'll be you will need to be the master
of machine learning as well as software
engineering and understand statistics to
these to its core and since this is a
very huge opportunity you will have
great opportunities to shine a via trial
and fire and but there will be less
guidance if you go around becoming a
data engineer because there is a lot of
jobs which goes because what does a lot
of data which you cannot actually tell
what you actually need so this job
requires huge analytical skills as well
coming to the next one the machine
learning engineer there are a number of
companies for whom their data is their
product so there are companies who
work on data like the data what they
produce is their actual product so they
sell your data or they sell the data
that they have gathered as their product
right so in this case the data analysis
or machine learning going on can be
pretty intense so the machine learning
that you'll be doing here is pretty
intense so you'll be applying a lot of
different kinds of model on the same
data by putting out different
constraints here and there and analyzing
the data as much as you can in different
ways to find out different results
because the data that these companies
are producing is the actual product so
the probably the most ideal situation
for someone who has formal mathematics
statistics and a good background in
mathematics is is someone who is very
much suited for this job and they're
looking for people who are very good in
machine learning as well so as the name
suggests goes as machine learning
another skill that might come in handy
is deep learning and AI so these people
will need to be those people who are
core at data analysis so you need
to understand different machine learning
models different machine learning
algorithms that you have and understand
where which model can help you under
certain kind of outcomes out of certain
kinds of data so if you are looking for
one particular outcome you need to
understand the data that you have might
suit one model more than the other and
one model might produce let's say a much
more accurate outcome than the other one
so you need to understand machine
learning to its core if you are looking
to become a machine learning engineer
and the last one is the data science
generalist so all of the things that I
mentioned before like the data analyst
data engineer and machine learning these
people are say since I've mentioned like
data science is like an umbrella term so
you have a lot of different things going
on you have people who are gathering
data there we have people who are
visualizing data you have people who are
working on R Python and different data
science models and different statistical
models some people are creating
statistical models for the data that you
have so these are the different fields
of data science so most of the jobs
sometimes focus on one particular skill
over the other although they would want
all of
you know all of them but they would
focus on one particular aspects like
some people are looking for people who
are extremely good at machine learning
but when you come to a data science
generalist yes these people are looking
for those people who are good at
everything so it is like master of all
not even jack is a jack of all trades if
you are looking for that
jack-of-all-trades job that is the data
science generalist
so he will be knowing all of the
different stages and the life cycles and
he will be involved in all of the stages
like from procuring data he needs to
understand how to procure that data how
to cleanse that data how to make a model
based on the business requirement then
actually code that business requirement
model into some programming language
like Python or R and then take data out
from that data and be involved in the
entire process is that is what I mean so
if you are a data science generalist you
have to be jack-of-all-trades know
everything pretty well and be involved
in the entire process and these people
are very highly paid because these are
these people are involved at every stage
and every aspect of the job right so
these are the four different job feels
that we found out are the most prevalent
in the world right now when it comes to
data science if you are looking at
responsibilities so we have generalized
that responsibilities because with
different jobs comes different
responsibilities as I've mentioned like
while I was explaining data analyst I
told you your major job would be to
gather those data through Excel and SQL
and combine that data and cleanse that
if you are looking for a machine
learning engineer then you will be
deeply involved with statistics and
machine learning and you'll need to have
a good background with mathematics
though so the responsibilities then
shiver around here and there depending
on which role you are going for but we
have generalized the responsibilities of
all data scientists that the world is
looking at right now so what you have to
do is identify the valuable data sources
and automate the collection process now
you cannot go about collecting data from
everyone manually right you need to
automate that process so that you can
get the data which you want and not all
the data that you will get will be
valuable so you need to identify which
data points are the
most important once based on your
business requirements so that is the
first one second one second
responsibility that you will be having
as a data scientist is to undertake the
processing or structuring the process of
cleansing that data - or preparing that
data from an unstructured and structured
format board so some some places you
will have structured data but there are
also a lot of unstructured data in the
world so you need to find a way to
process that data and find out the
relevant details in that data which will
suit your business requirements right
then when you have all of this data you
will be expected to analyze this large
amount of information and discover the
trends and patterns what is trending
right now if let's say your business
requirements asks you if your company's
line launching let's say a new product
in the market will your product do well
bear will your product do well so you
need to understand the trends across the
different cities let's say let's say I'm
launching a food product in let's say
India so you need to understand how the
trends are going on in the particular
state of India and where your products
will do well so you can do a trial run
on it and you need to analyze the deal
in rows data's to give out the trends
and patterns so that people can actually
go about launching their business right
then you have to build predictive models
and machine learning algorithms on your
business requirements but so this is
another Job Description or another
responsibility of a guy who's doing data
science right you need to build those
models which suit your particular
requirements there's no generalized
model which predicts everything you need
to be specific so that you have the most
accurate data that you can bring the
next one is to combine those models and
assemble combined all of the models you
have so you won't be just analyzing data
of all the data you have based on one
particular mathematical statistical or
as well as one algorithm with in my
opinion so what you'll be doing is
you'll be gathering all of the data from
all of these models and then you will be
bringing up a new model which can be
most beneficial for your company
then you need to present whatever
the findings that you have so if you
have whatever the findings you have let
let's say fifty Freak six percent of the
people are preferring my food product in
let's say Jenny so I need to plot this
information into why this is happening
into a chart format or a bar graph or
something so that my stakeholders
understand we're launching what product
or where which kind of service will reap
out the most amount of benefits or even
this is about the benefits but there can
be even points where people can analyze
data where bear your service is failing
so there are a lot of service based
companies so they have to understand
where their service is not doing well
and what they can do to improve that
service in one particular area so you
need to visualize those data so that the
stakeholders can understand what can be
done to improve their business model and
then you need to propose solution and
strategies to these business challenges
so not just telling that there if we
have a date let's say n number of data
on the same product that I mentioned
before my food product you can say that
my product is not doing well in let's
say again Chennai it's not doing very
well in Chennai so what is you can just
tell them that this is not working well
but the stakeholders will be looking for
answers like what can we actually do to
make that product work in chaning as
well so you need to implement or you
need to implement some models so that
you can extract data extract information
from data which can tell solutions and
strategies or what you can do to make it
more profitable about your so that your
business goes into profit so you can do
that through data science and finally
you need to collaborate with engineering
and product development teams because as
I mentioned you need to tell this to
everyone in your company you need to
communicate your findings and
collaborate with everyone so that your
company reads the entire benefit of the
data scientists that it has right moving
further so what are the skills that the
data scientists have so and now we'll be
talking about different skills that you
need to have to become those data
scientists who are well paid so if we
were looking at different job
descriptions
and we have come up with all of our
research and which are the most
sought-after skills in the data science
world right now so we have mentioned all
of that and we have also plotted a heat
map of which skills are more
sought-after than the other so we will
be discussing that in this section so
the first one that comes into mind is
programming skills so you need to know
how to program your statistical models
in an R or Python it is a very vital
skill it is the first step I'd say to
becoming a data scientist you need to
have a good programming language because
you need to do sophisticated programming
so that you creep out the best benefits
like also you need to understand a
queering language like SQL or no SQL or
anything which from which you can gather
data it's it's a very good start to have
these disinformation in your bag and
mathematical skills are always a
positive when you are programming a
statistical model so you need to
understand the maths behind it right so
that you can maybe let's say do that
with the lesser complexity in your code
then understanding the statistical
skills so you need to have good
understanding of Statistics that this is
very important because all of all the
things that you are doing is basically
maths you need to understand the
statistics behind it how you can use
different kinds of data points and how
you can do regression and different
stuff to your data so that is very core
element of data science that is the
statistical skills so you need to be
familiar with a lot of different kinds
of statistical test distribution models
then there is a estimators likelihood
probability statistics and this having a
good background in statistics can help
you with machine learnings as well sorry
machine learning as well
because a lot of the machine learning
algorithms are also based on statistics
so you need having a good statistical
skill is very very crucial in becoming a
data scientist and this is something
that you can develop if you even if you
are not very good at 6 right now you can
read up on it and become very good at it
then understanding of machine learning
so knowing the different algorithms and
where they
work so this is very important because
when you are working as a data scientist
you will have a lot of data like a lot
of data and that data might not be
structured it might be unstructured and
you'll have like lakhs of columns so
understanding which algorithm will
produce the best benefits out of those
lakhs of columns and taking which kind
of constraints in is the crucial factor
right so that is where machine learning
comes into play so you need to
understand machine learning very well
and anything that you are looking to
apply in machine learning again you'll
need programming skills for that to
implement those in Python or R and more
knowledge of having good and knowledge
of making new models is also a plus so
you can also create new learning models
for your given business requirements
which goes into a plus next you will be
looking at a very good business acumen
so if you are looking to be a data
scientist who is highly paid who has the
most number of perks in the world so you
are looking at some someone who has
great business acumen so what do we mean
by business acumen is that you need to
understand the industry that you are
working with so data scientists can work
in various different varieties of field
you could be in the health industry you
could be in the food industry you can be
a product industry you can be in the
service industry in even in that there
are sub domains so you need to
understand what the market is what the
people are looking at right now so you
need to understand the trends of your
business so if you understand that then
only you can apply proper models to your
data so that you get out get the proper
outcome from your data and predict what
is going to happen properly and make
good suggestions to your stakeholders
about your business so this will
basically having a good business acumen
will help you understand the problem and
how you can go about solving the problem
it will come to you inherently if you
understand the business domain that you
are working with or the industry that
you are working with next thing that you
need to have is good data visualization
skills because I have been mentioning it
again and again even though you are very
skilled not everyone will be
exceptionally good at computer science
or statistics so you need to
tell them this is my findings and you
need to plot that in such a way that
it's easily understandable for everyone
so that based on your understanding or
the data that you show them they can go
about making business strategies and how
they can go about implementing changes
to their products which will reap out
the most amount of benefits after that
is data wrangling so again it is the
ability to process and work with messy
data so as I mentioned before lot of the
times that you will get data it will be
unstructured it will not be in a
structured format where you have
everything that you need no not
necessarily many times you will have
faulty data or some some places you will
have missing data points so you need to
be able to work with that messy data to
cleanse that data into something which
is usable so handling that inconsistent
format or inconsistent data is very
crucial when it comes to any data
scientist in the world so any data
scientist in the world has to understand
how to work with messy data if he wants
to be a more successful or reach the sky
in his career and according to different
websites and different job descriptions
that I went through so these are the top
five most sought-after important skills
in data science field so you need to
have very good understanding of machine
learning you need to know Python and R
so that you can apply on machine
learning and AI modules that you have
into actual code then you need to have
an understanding of SQL so that you can
get the data that you want to apply
those machine learning and AI model too
and finally you need to understand big
data in Hadoop which is also very
important because you will be dealing
with huge amounts of data so
understanding
Hadoop and all is massive plus when it
comes to data science now this is a heat
map that we have plotted so if you are
looking at different skills of data
scientists knowledge of programming
tools is very important data
visualization and communication is very
important data intuition is basically
looking at data and fire
directly knowing which constraints will
be the best for your business model is
very important good understanding of
statistics is very important machine
learning is very important and few of
skills which are on the lower end but do
help build your car to do help in
building your career as a data scientist
is software engineering and software
engineering models that have like water
for water flow model and different
models which are at least offer
engineering understanding of that it
comes as a positive but it's not very
much important multivariable calculus
comes in handy while handling like very
exceptional data but it's not
necessarily very much required or
sought-after in the world and so same
goes for linear algebra as well so these
are a little much on the lesser side of
important things that you must know but
having a great grasp on machine learning
statistics and programming tools and
visualization tools is very important
let's talk about the average salary then
so as a data scientist if you are
working in India as a junior data
scientist you are looking at ten LPA now
for any fresher that is out there ten
LPA as an average is a huge bangor
number when it comes to any in the
indian society then LPA for any fresher
as average is a huge amount of money and
if you are a senior developer let's say
with three or four years of experience
you are looking at twenty LPA so with
little bit of experience as a fresher
you can go about on approximately 2
lakhs per month and that is a huge deal
when it comes to average salaries in
India and speaking of USA we have junior
data scientist landing at about an
average of 88 thousand dollars and if
you are a senior at a scientist you are
looking at an figure around 125 thousand
dollars again huge amount of money and
as mentioned before this is the sexiest
job of the 21st century according to the
Harvard Business Review and they are not
lying because if you go about searching
for jobs in data science field these are
the
highest and most well paid jobs with
huge amount of perks when it comes to
any jobs in the world can be labeled as
the number one job in the 21st century
now let's talk about the Learning Path
so I've told you a lot about what data
science is and how you can become a data
scientist different skills that you need
to have to become data scientist so now
that I have told you that you need to
learn this this and that where do I
begin where do I start my journey as a
data scientist so don't worry I have got
that covered as well so what you
basically need is the basic W that is
the watts and whose and wise of data
science then you need to have the basics
of mathematics part one then you need to
have machine learning concepts spot-on
deep learning concepts pattern and a few
database technologies that you need to
understand and finally data
visualization so these are the different
things that you need to know so your
question must be where do I learn all of
these so you don't have to worry v at
Intel II Pat have carefully designed a
course for you so that you can become
the best data scientist in the world let
me show you that course real quickly so
if you come up here on our website this
is the data science architects master's
course so in the what we have understood
from our different we have also applied
a little bit of data science here in
there and from all of our customers and
their reviews what we have thought found
out is understanding of few different
things that you need so we have put up
like not all of the people require an
advanced training on Excel and MongoDB
on mssql because they have so we have
put that as in the self-paced because
you can learn it on your own and go
about learning it professionally as well
and then we have put up few important
concepts which are much more required in
the online training program where you
can learn it online with a live
instructor so the concepts that we'll be
covering there is Big Data Hadoop and
spark which is and then there is data
science with our so data science without
again important machine learning
concepts will be teaching you will be
teaching you a AI and deep learning will
be teaching you Python for data science
as well because most of the job
requirements in the world that we have
seen sometimes demand are sometimes
and pythons so we are teaching you both
so that you don't miss out on any
opportunities that you need
we'll also be teaching you data science
with SAS and finally where we have put
up a data visualization tool here in
here as well so tableau for desktop is
again a data visualization tool so we
are covering all of the different
aspects that you need to become a data
science master or architect master's
course right so what is that that in
Delhi Pat is offering you is that
separates us from the rest so we are in
Delhi Pat understand that you need to
have an inter instructor-led training as
the last self-paced training because not
all of the times you will be free
sometimes you think about that learning
new things at 3m as the night so we have
provided you with self-paced training on
all of these different concepts so that
you can go about and learning on your
own whenever you want to and another
thing that we have offered in there is
like if you are learning at 3:00 a.m.
you might have doubts right and you need
someone to clarify that curiosity that
you have for that we provide you 24 by 7
lifetime support now what we mean by
lifetime support is you have sorry you
have a lifetime access to our courses as
well so what we mean by 24 by 7 support
is if you are reading let's say at 3
a.m. and you have some doubt you can
reach out to our team 24 by 7 and we
will reach out to you immediately
second we offer you lifetime support
lifetime access on all our courses so
let's say there are some new models
which are coming up on new advancements
in the tableau and there is something
new in those tools or it gets an
upgraded version so in that we will be
putting out new content so if you ever
want to learn about the new Canon you
can come back to us and read anytime you
want so we provide you with lifetime
access for that so that you are data
scientist through and through who is
with the trends so whatever the trend is
going on whichever tools are working
right now you can have accesses through
our course we will be updating it
constantly based on what the trend is
and then we provide you with the job
assistance as well although if you have
data science on your field and if you
mention all of the things that you have
you actually won't need our job
decisions because you will have a lot of
opportunities but even if you do need
we do provide job assistance for you as
well as we provide you the output the
advantage of flexible scheduling so if
you miss the class and you let's say
some trainer is is does not teach in the
way that you like we have multiple
trainers who are industry experts and
you can go about shifting and finding
which trainer suits your style and learn
from the best trainer all of our
trainers are very good but some some
teaching patterns are different from the
others and we understand that that's why
we give you the option of flexible
scheduling as well you can share your
batches according to your needs and then
finally if you do well you can get
certified with us and this is a global
industry recognized certification so if
you show any company that you are
certified from Intel epad that means
something right and if you have any
other queries you can talk to our course
advisors about the course here here are
the few different things that we'll be
teaching in your teaching you in the
Masters course will teach you MapReduce
and real-time analytics with spark we'll
teach you a lot about logistics and
regression logic logical regression as
well and then we'll teach you deep
learning and AI models you can check out
the details of the different course
modules if you want to on the one on our
website which is live right now and this
is the learning part that we have
provided you with so we have a week wise
which week will you be learning what so
in the sixth week you will be learning
data science with our 12 week will be
you will be learning data science with
Python so we have made this learning
path for you so that you have the best
outcome of our out of our course so this
is how you can go about becoming a data
science architect master and if you
finally complete our course this is the
certification which you will get and you
can see it is an IBM recognized
certificate right here so an
industry-recognized certificate with
IBM's name on it means lot so you can
get that if you complete our course and
if you want to check out a course rating
and reviews here are all of it and our
customers they have given us some a
raving reviews about how good the course
was and how much it helped
expand their career right so let me jump
back to our PF presentation real quickly
and finish this presentation so if you
are interested about learning more and
you want to check out our content so we
have intellibid blocks which are free of
cost you can go about trading various
kinds of technologies that we post
tutorials and different responsibilities
responsibilities a lot about the
technical content we put out a lot of
good content on our blogs which you can
go about read for free if you are
looking for a video based tutorial you
can check out our YouTube channel you
can find out all the data there and
there you'll find everything that you
need so and finally I would like to
leave you with the words an investment
in knowledge always pays the best
interest so invest in your knowledge
work with us learn learn learn don't
stop and you'll have a bright future
also guys since you have been here
towards the end of this video session
you can use YouTube 30 to get direct 30%
off flat 30% off on our course so you
can just go about right now on the
website and get 30% off directly and
thank you
