So before we start with today station so little
bit of background about me I have almost more
than seven years of experience in IT delivery
and consulting and I have worked in diversifying
Domain but mostly I have dealt with different
kind of data set and I have been helping our
client into giving different kind of big data
solution but I have broad knowledge in Big
Data Analytics cloud and iot but my passion
is more into the data science and so that's
that's the time I used that area into data
science and from there on for last 45 years
I have handle different kind of pollution
like in pre sales manager in data science
business concern then and helping data scientist
as well to groom into their area and in terms
of analytics project you can talk to me about
a different kind of traffic light supply chain
sales customer marketing and BPO so feel free
to ask your question as I said I would be
taking the question in the end after 10:45
so normally I would like to understand the
audience and their expectation but since I
won't be able to ask the same thing for all
of you so probably we can quickly start with
the station and as I said while you post your
question from you can give little bit of your
background so this is the agenda for today
so first we should start with high level data
science application what is happening right
now in industry what is going to happen in
future but will also understand at what point
we are at so why today and why you should
learn about this things I would be discussing
about that then the second would be very much
interesting so whoever has started just Google
in what is data science what is analytics
what is a I am sure you are quite in you have
already been interested that's why you are
joined this but still there are a lot of confusing
terms of those terms I would be clearing out
what is machine learning what is AI what is
big data how to join big data and data science
and create Big Data Analytics pipeline and
then would be also talking about a case study
which is about fraud analytics and I think
these second part would actually clear your
thoughts around this and you would also understand
which aquaria to choose so that I would be
talking mostly into III section where I would
be talking about what are the opportunities
what are the different kinds of roles are
there what are the kind of companies are hiring
and and what is the market you have so that
you can actually get into that area and I
would be also explaining that it doesn't matter
with water background you come from or whatever
experience do you have you had some role in
data science even if you have 25 years of
it experience even if you don't have any experience
at all in it still there is a role for you
in data science which should be interesting
in this III section should be with us and
feel free to ask your question but we would
be starting with a small video I hope you
would be hearing about Sofia Sofia the First
AI robot and would be actually looking at
a snippet and then probably we can start before
this. There was couple of question but the
robot was actually given all those question
and answer so it was quite easy for her but
now the question is coming those are actually
random question which is coming at her way
now listen to the question and definitely
if you have not heard about her interview
would be surprised so you might think that
is not the correct answer but he intentionally
ignore that question because that might create
a doubt and controversy around Sofia that's
why she intentionally ignore that question
sorry about that
I think I think that's enough and probably
that is why I chose this video because still
people and thinking it's about only hi. Hi.
If you do a Google search and YouTube then
you would understand that there are a lot
of negative article about Sophia that this
is nothing but a program but to be very honest
I am also quite excited about the future and
I hope that I can in this one 1 hour and 40
minutes I can also create lot of interest
among you so late understand what already
happened in this last 100 years what happened
if you see the big Industrial Revolution so
we are at 4 Industrial Revolution 4 but what
happened in last three the first one was when
we actually created invented steam engine
so we experience the industrialisation and
urbanisation around lot of different industries
and that time productivity was around 0.3
% then I come with the early Robotics where
we are talking about the production line so
there was mass production from electrical
power oil and steel but still the productivity
is like 0.4 then come the use of it which
came on 1995 to 2005 so using computer and
internet we could achieve lot of productivity
until this. Everything was fine and where
are growing at good place but something happened
around 2015 and which actually go on till
2065 and which I am not saying this is actually
from McKinsey paper you can get it as well
that this is going to come from automation
which is industry for industry 4.0 right now
we are looking at it and this productivity
would come from fusion of different Technologies
suggested come from digital and physical due
to artificial intelligence big data cloud
and iot and if you just search about this
which is actually Hype cycle if you just search
about Hype cycle Gartner Hype cycle of Technology
is emerging Technologies there are so many
Technologies right now in the height cycle
which include deep learning machine learning
connected home lot of things but one thing
is evident that these and which is artificial
intelligence machine learning and deep learning
these three will actually exist all the other
future developments and all the other Technologies
so you can talk about cloud but you can get
the highest efficiency when you come by in
cloud with you can be a very good IIT engineer
but you can get highest use usage of it when
you combine iot with a n m n i am not explaining
all of them but I would actually ask you through
couple of industry use cases which can probably
a give you enough excitement in your own domain
that how this is going to change everything
so it doesn't matter whatever industry you
come from say when you coming from agriculture
so there are so many use Kesar happening in
agriculture I am sure you would be hearing
about Drone so right now Drone can be passed
on to the field and they can actually capture
all the humidity and soil master and all the
stuff and based on that probably you can come
up with a machine learning algorithm which
can tell you what is the kind of what is the
kind of pond or wheat or rice or whatever
you should actually be so that gives you more
productivity similarly in Automotive in Healthcare
in telecom there are thousands and thousands
of use cases where machine learning can be
used this is also from am I can see paper
you you can search about it that there are
so many industry and there are so many use
cases but I would be talking about couple
of use cases today but I want to give a Framework
so that once you go back from here you can
also learn about these use cases and you can
utilise that knowledge in your own domain
in your current current age job so one there
are three lenses so there are three lenses
by which you can look at any any application
so first lens is about your industry lens
so which industry you are working on suppose
you are IT Engineer but you are working on
bfsi PUC there would be whatever the application
your building till last 5 years problem you
can say you changed their sunao fraud can
be detected by machine learning the credit
scoring can be done by machine learning if
you are working on Telecom earlier you are
building application where whoever was leaving
for tomorrow you can see a machine learning
application which is not only telling you
who is leaving your Telecom network brother
who is going to leave the predictive analytics
can be also done similarly if you are in manufacturing
then normally see that what are the parts
is going to be broken so that is predictive
maintenance even before the path got broken
it can be predicted similarly for transport
and Logistic also everyday Paytm Flipkart
Amazon are using this transport cost Optimisation
so that they can tell you that what time your
product should be delivered so that the guy
the delivery guy can reach maximum home similarly
there are plethora of example in different
industries I can talk about it I am in 6 hours
and 7 hours but still that only cover similarly
in terms of business function also if you
are in if you are doing SAP CRM then you know
the customer and analytics but you would see
there is a you change going on in near future
because everything whatever you used to do
in SAP that we now driven by machine learning
similarly you are if you are in to sell side
of 8:00 UTC that whatever you used to do in
excel prove that is going to be replaced by
machine learning Technology all the business
function it can be HR it can be supply chain
it can be fraud everywhere machine learning
is going
to come
