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