driving any data-driven business to
success is possible by making use of
concepts such as data science and
machine learning many companies from the
startups to the fortune 500 like Apple
Microsoft Google and many others go on
to use these concepts on a daily basis
for their needs and in this session
we're gonna compare data science and
machine learning head-on well before we
begin with the session make sure to
subscribe to the Intellipaat's YouTube
channel and hit the bell icon so that
you never miss an update from us here's
the agenda for today we'll have a quick
introduction to what data science
actually is and understand data science
basically and after this we're gonna
have an introduction to machine learning
and see where machine learning is used
and much much more and after this we're
gonna have an in-depth comparison
considering a lot of factors about data
science and machine learning and see how
both of it holds its ground together as
well and after this guys if you have any
queries make sure to head down to the
comments section and do let us know and
we'll be happy to help you out and in
case if you guys are looking for
end-to-end Co certification in data
science Intellipaat provides the data
science architects master's program
where you can learn all of these
concepts thoroughly and earn a
certificate in the same as well so
without further ado let's begin the
class coming to the first point on the
agenda it is introduction to data
science we need to understand what data
science is guys here's a quick overview
on it so basically data science is
nothing but a multidisciplinary field or
focus on the multidisciplinary word
basically which helps in finding
actionable insights from large sets of
raw data and structured and unstructured
data at the same time so when you talk
about data science is basically you know
takes an unruly entity a raw entity such
as data and converts it into something
valuable something useful for example
information so this process of
converting data into information and
doing it in the very efficient way is
basically one of the goals of data
science and you know the main goal if
you have to talk about a data scientist
perspective is to basically ask
questions on the data to pretty
much you know find out where all the
data can be studied from basically
potential avenues of study and pretty
much you know get answers to specific
questions and ensure that you know
that you know when and where to ask the
right question and to ask the perfect
question which fits that scenario of
regarding whatever data aspects we're
talking about and this skill of asking
the right question this skill of or you
know finding out something about your
data which might not be present for the
naked eye is one of the biggest goals
one of the biggest skills in fact to
have as a data scientist so if you have
to talk about data data is considered to
be the new oil because we all know how
much worth oil is right so data it's
pretty much is the biggest component in
today's world of technology and this is
because everyone understood the
potential of data everyone understood
what data can actually pretty much drive
for their businesses driving them to
success is one of the biggest aspect of
it getting good insights predicting the
future and performing analytics and much
much more and at the end of the day this
will of course you know increase
profitability by somehow for all these
companies which have been using and that
that could be one of the main reasons
why data is in the spotlight today and
to quickly talk about the impact of data
science we've seen companies such as
Apple and we have Tesla so Tesla has the
self-driving autonomous cars and Apple
has Siri Siri has a chatbot and when when
you go on to learn more about these
concepts or when you want to practically
use them you understand that these
concepts are such beautiful innovations
of today's world they make use of
concepts such as machine learning
artificial intelligence and all of these
comes in fact under the umbrella of data
science as well and I read about the
practical use case of detection of
breast cancer which happened as an
experiment in 2019 oh where a lot of
researchers actually are detected about
a 50% chance of predicting premature
breast cancer and trust me 50% is a huge
number when you have to talk about
anything in the field of medicine now
let's talk about an airline for example
Southwest Airlines is again a very good
airline which runs throughout the United
States so these guys saved hundred
million dollars by basically analyzing
how long their planes actually waited
with their engines on before taking off
and pretty much they could come down by
a number of hundred million dollars
that's a huge saving for an airline
right
then you have to talk about UPS UPS
basically saved 39 million gallons of
fuel by just optimizing how they deliver
packages so UPS is basically a very
famous package delivery service across
the world and a courier service to be
honest so these guys analyze the routes
probably rerouted it very efficiently to
understand you know how better can they
produce routes how better can they
actually follow along by delivering
packages and they save 39 million
gallons of fuel ladies and gentlemen and
if you have to talk about a quick
introduction to machine learning well
what is machine learning is the first
question you're supposed to ask well you
know machine learning is an application
of artificial intelligence to begin with
of course because at the end of the day
machine learning whenever we talk
anything about machine learning it is to
achieve AI on certain level it might
be Weak AI it might be strong AI or whatever
it is so machine learning basically you
know provides computers the capability
to learn on their own and to improve or
with the experience that they've been
using to learn and all of this is done
without basically programming them just
a quick info guys in case you guys are
planning to have an end-to-end course
certification in data science
Intellipaat provides the data science
architect master's program where you can
learn all of these concepts thoroughly
and earn a certificate in the same as
well the link is in the description box
so make sure to check it out and on that
note let's get back to the session and
if you have to talk about machine
learning right now well right now it
basically is a lot of usage of
algorithms mathematics and statistics
and all of these on steroids but in the
future machine learning will be all
about achieving artificial intelligence
as I just told you even right now we are
striving very close to it but here when
we have to talk about cognition and
achieving artificial intelligence as a
whole we compare this to human level
intelligence and then work with it and
of course this future is very
foreseeable it's very near and probably
by the end of this decade we will have
multiple revolutionary concepts multiple
revolutionary tools and techniques which
have which will basically help mankind
to you know get one step closer to
artificial intelligence so there are no
multiple machine learning applications
around as a field look so we have voice
recognition we have social media we are
way video surveillance malware and spam
detection predictions and for and many
other uses as well if you have to talk
about voice recognition again voice
unlock is a very famous use case of
machine learning then if talk about
social media or Facebook has Auto pretty
much it recognizes your face
automatically then Instagram knows what
ads it's supposed to show you Twitter
our pretty much analyzes the sentiments
of the tweet Vimeo YouTube Skype all of
these guys make use of machine learning
on a daily basis if you have to talk
about video surveillance think about
automated traffic finds a system where
you know that there doesn't have to be a
cop there doesn't have to be a policeman
you know finding people who are
violating the traffic rules it might
just be a camera which is smart enough
which has a very good framework and it
can pretty much capture people who are
you know going against the rules and
find them automatically and then if you
talk about malware and spam so how does
for example let me take the example of
Gmail so how does Google know what mail
is spam what mail is contains a malware
and all what mail is it supposed to give
it to your inbox so Priority Inbox as
well so you know we need to talk about
this again yes machine learning is being
used here for a really really long time
but it was so subtle pretty much that we
couldn't notice and then you have to
talk about predictions of the future of
course there's this concept called as
data analytics where we use the present
data to basically analyze understand and
plot a data in the future timeline so
that again is a very good use case of
machine learning as well if there is
anything else do head to the comment
sections and do let us know and of
course here are some of the key moments
in machine learning system in 2012
Google came out with Google brain and
then deep mind deep mind basically is a
brainchild of Google where it could play
games at a mind-blowing level it could
pretty much you know analyze millions
and millions of steps you know in a game
every single second as well then in 2014
we got deep face deep face was basically
Facebook's brainchild and they pretty
much wanted to implement deep learning
for face recognition as I just told you
a few sites back then of course alphago
alphago is basically similar to deep
mine it was part of a part of a program
under google deepmind where there was a
game called as go and machine was pretty
much put into place where it defeated
the world champion of
the game go multiple times and go is
considered to be the toughest board game
in the entire world so it was predicting
about a million times faster than the
human being a million yes and this was
every second so you know humans cannot
think that fast every single second so
this just goes on a very exponential
scale with every second passing so now
coming to the direct head-on between
data science and machine learning the
first point we'll be discussing is the
meaning so data science you know as you
know it is basically a field where data
any any type of data structured data
unstructured data semi structured data
it goes through a process of being
cleaned filter and basically analyzed
and all of this is done to ensure there
is something useful which can be put out
in the other end of it and that result
can be used effectively as well so
that's data science coming to machine
learning machine learning is actually a
part of data science which makes use of
multiple tools multiple techniques out
there which creates beautiful algorithms
so these algorithms are the basic
foundation the fundamental aspect of
where and how a machine can learn from
data by making use of the experience
then coming to second point it does this
scope when if you talk about the scope
again data science has a vast scope
because you know if you talk about it in
single dimension in today's world data
science has been everywhere it has a
foot which is so so strongly put in the
world of data that you know every
company which is driven by data use data
science in one way or the other then you
have to talk about machine learning
machine learning is basically a part of
data science as I told you and of course
it is this part that it pretty much
talks to it is the data modeling stage
so once the data modeling stage is
completed the machine learning part of
data science will be done and this again
is one of the very most important
differences between data science and
machine learning when you have to talk
about the third point it has
methodologies of course you know data
science works with multiple manual
methodologies but when you have to talk
about making a machine efficient but my
comparing it directly to a machine which
makes use of algorithms data science
lacks a little and machine learning of
course cannot exist without data science
and all of these data which machine
learning algorithms used to work so
efficiently have to come from all the
other data science concepts to where you
know models are basically preconditioned
pre treated data cleansing is done
pre-press the data preparation
is done and then later these algorithms
are basically applied to create a model
to train these model to test if it's
working to optimize it and further on
coming to point number four it's the
goal of these technologies so basically
as you already might know it data
science you know helps you to define new
problems that actually need to be solved
in today's world so instead of giving a
direct solution to all the problems
which already exist this will define new
problems and these new problems have an
a point of answering and this answer
comes from machine learning all the
techniques of machine learning all the
statistical analytics methodologies and
much more and of course machine learning
you know it knows how the problem is
sorted out and pretty much it helps in
giving the solution to the problem it
has all the tools that is needed all the
techniques it is needed to basically
generate models around the problem and
to solve this same coming to point
number five it's prerequisite to
understand our data science you know
there is a prerequisite of understanding
SQL SQL a structured query language and
this is needed because when you work
with data you'd be talking to databases
if you have to talk to databases if you
have to talk to your data present in the
databases you need to understand how you
can create tables create databases work
with your data alter your data delete
your data and much more SQL helps just
there when you talk about our machine
learning machine learning requires a bit
of programming in depth because
languages such as a Python are Java Lisp
or all of these concepts are the ones
which basically implement the
mathematical concepts of statistical
concepts and which provide the
foundation to or you know basically help
the Machine understand the mathematical
algorithms on which it has to use these
concepts and work on point number six is
the actual process so data science you
know is basically a complete package it
is a complete process which involves a
lot of things as I told you everything
from data cleansing to data analysis
comes in the field of data science but
when you have to talk about machine
learning machine learning is just one
part in this huge world of data science
guys then coming to or the next point
which is basically the average salary
data scientists get an average salary of
somewhere or 130 thousand American
dollars per annum
but then you have to talk about machine
learning engineers machine learning
engineers also get a
attractive compensation of somewhere
about 124 thousand American dollars per
annum as well so these both are very
lucrative carriers and they pay really
well and they are among the top jobs in
today's world then we have to talk about
the companies which go on to use these
technologies let's talk about data
science you know everyone from unisys
IBM fractal analytics we have Eva Ernst
& Young Edgeworth Mu Sigma Ola cements
or I could true caller and thousands and
thousands of other companies basically
go on to use data science for their
daily needs
coming to machine learning machine
learning in today's world has a very
strong holding in the social media
industry you know everyone from
Instagram LinkedIn Pinterest viber
whatsapp Evernote and read of course
brainchild of Google we have Twitter we
chat Facebook I can you know pretty much
name everything out there but then these
are the main aspects where experiments
were tried out a couple of years ago of
using machine learning most efficiently
and as per a survey conducted in 2015
pretty much machine learning was used
the most efficiently in the world of
social media and you know there are
other companies as well as Adobe Best
Buy Apple Walmart Google BBC Skype and
of course thousands and thousands of
other companies and all the fortune 500
companies pretty much go on to use
machine learning in one way or the other
to basically summarize this discussion I
would like to tell you two things one of
that is that data science has been
rightfully called as a new oil in
today's world of information because
again having data is like having wealth
if you understand how you can use it
think about crude oil you cannot put
crude oil in your vehicles as it is it
has to be converted into petroleum into
diesel and whatnot right so data science
is just that tool which basically does
exactly this when you have to talk about
this analogy with respect to data then
coming to machine learning of course
machine learning has absolutely
revolutionized the way we make use of
computers the way we treat data the way
we understand data and we the way we you
know go on to make data work for us so
these two are very lucrative careers
they're the top jobs in today's world
and of course they are very magical to
learn to understand and of course to
build a career in so on that note you've
reached the end of this comparison just
a quick info guys in
you guys are planning to have an
end-to-end course certification in data
science iIntellipaat provides the data
science architect master's program where
you can learn all of these concepts
thoroughly and earn a certificate in the
same as well the link is in the
description box so make sure to check it
out hope you guys enjoyed this video and
got to learn a lot from the same as well
if you have any queries or if you have
any more points you want to add to this
verse this video make sure to head down
to the comments section and do it now
we'll be happy to help you there and
reply at the earliest and on that note
have a nice day
