okay so uh my name is Alice and I'm a
senior data scientist in an instigator
organization in India and we saw
multiple man and AI perverts
so basically prayer desire is working in
multiple companies and a half four six
seven years of experience into the
designs and have been teaching realistic
things from the past few years so what
we are going to do in this session is
just add a more introduction class it's
going to be more fitting for acquisition
of course I mean what we do few things
but it's more of an interactive session
wherein you get to know what are what
what exactly you are getting into right
what is data science why is it used
where is opportunities and all those
things right so let's try to keep this
as interactive as possible so that your
questions gets transferred and I will
come me what I had to remain this
session right I think yeah okay so first
thing first
why why do you think data science or
Anita angular takes AI all these jobs
for certainly in boom right now or why
is it trending right now any thoughts on
that anyone can answer which is go ahead
and participate in the discussion so
that it will be good so why do we why do
you think that we are having suddenly
trending jobs or opportunities
the work that we are doing why why we
think it's it's trending right now India
terminated work module any thoughts on
this oh not much sure anyway yeah so
it's basically because of multiple
reasons why we are seeing that pitch now
it's because of the reasons an average
two down Nikita has identified so large
and huge amount of data has been
generated in recent years due not to
this extent bad thing but corpus of
Internet and the social media and so
many XYZ things data has I mean every
organization also has collect started
collecting their business data so long
that we have you want of data anyone
wants to know what is what is going on
and then try to find out the inside sort
of the data so that's why it's it's
everyone is is trying to make different
decisions so that's why we are we are
seeing behavior that is moving right the
data and predatory decisions and the
other two important things are the
computing power itself right so as you
see the Google and Amazon's we have like
CPU GPUs and CPUs and everything they're
able to make it faster right so we are
able to run our queries really faster
and get to the insides so you know one
of the reason for yeah it was always
I can even one knew what is it clean
okay
yeah I didn't know someone else John
safe oh hey hi safe hello
say can you hear us safe can Amira's
Sapphira couch - could you please admit
from you alright fine actually
please go ahead and okay yeah sure so
yeah and also we haven't seen that there
is a lot of unstructured data even that
can be used to get insights so even that
has been trained all these reasons what
he can see is jobs which are coming out
of the teeth allocated if it can be done
it is various data scientists mystery
line here nappy so on so forth so that's
that's why here in this era of data
science so I mean now that we have that
what are the skills that are actually
required to be part of this right so it
is not spy you guys for here to acquire
those skills and become data scientist
so what are the different skills which
are required so that
that that journey becomes easier right
so a data science is a multidisciplinary
chawl rights it's not just one skill but
it's actually combination of group of
skills and that's why it's hard to gain
deposits not pretty straightforward
right so it comes with mathematics it's
all it's called statistics as well a bit
of mathematics linear algebra and
statistics there is what is required
from the mathematical point of view
right what is you know what is maybe you
know correlation what is depletion what
is goes to completion so these are the
aspects that we need to know from the
mathematics part of things a lot of
things of course and along with that we
need to have computer science knowledge
as then so I mean when I say common
sense knowledge it's it's not in depth
knowledge it's basically
programming languages so maybe our
Python size so we're going to use these
kind of tools but we won't be required C
C++ or Java so it's more of an advanced
programming tools I would rather call it
as a tools not languages and jobs we
will use they use them to process the
data right and so those are the two
things which which are required but and
the most important thing that that is
required is the domain expertise itself
right so you cannot solve the problem
people don't know that domain itself for
example you are working your data
scientist in a banking domain so you
don't know how banking your mind works
what are the problems
what are the different things that can
be done you can never survive the right
you should also the domain expertise
will also play a major role to to become
a data scientist right so this is the
main three important skills which are
required and of course there are more
skills but which will be part of these
things right so yeah so again I think
the the couple of doubts of the
classifications which usually people
have is what are the for the difference
between different roles right so one has
data analytics and business and maybe
data science ml machine learning right
so what are the difference between these
three things is something that usually a
students ask me right so that's what I'm
just trying to add try to explain it in
a session so the devs are cleared about
that so yeah basically data analytics is
is you are trying to understand what
happened in the past right so you
basically have the hiv-2 you
he's a meaningless zoo before you go -
okay yeah Yossef sure can you hear us if
I can yeah yeah you yeah yeah sure okay
so we are now that you say can you can
you tell me what the difference between
these two things you have a capacitor if
I can clearing your a cleaning about but
you know science no I don't know about
that but machine learning I know money
learning something getting the previous
data learning from the previous deltas
is something there and paid the
knowledge or that theta is negative
regulation and the logistic regression
yeah yeah sure thanks pensive anyone
else okay sure no shoes all these
commanders happen in the past so you
have the data and you are just trying to
understand would have in the past for
example just so you just need to
understand what happened in the past few
years
what was the sea doing what is the
product right all these kind of things
which you basically try to roll up the
data and
and trying to understand what is going
on in the business right so that's the
Utterson and Louie we wanted to know the
reason why as well right
for example revenue is going down in a
past one year now you want to know why
the revenues moving the question wine
has started we started asking why and
that comes into picture when you use a
bit of statistics and I mean the teacher
and also your business knowledge I try
to try to understand why the action is
happening right and and you try to find
out the reason okay my rent is going
down my delivery boys are doing huge or
my teacher is so that's that's what we
mostly do in the data sorry business and
it's what and it assigns ml is where we
do try to predict what's happening in
the future right so what will happen so
you use universe machine learning
algorithms and I did what his future
that's where you are running an equation
lost accreditation and addition to these
a lot of machine learning and the three
things which we need to understand and
that's what we will be going to learn in
the going for and also this there are
so yes structured data is enough right
if you have the column needs and you
have the rules and you can just write so
in to take simple example whatever data
sex leader that you attract in Excel is
also structured right so it will have
columns and will have rows and it can
save your data sets or data or data
values right but
so to come to the unstructured data you
can not specifically point to something
in index that writes the example text
face and image files audio video right
all things all things will fall into
unstructured data right for example you
just take your review from any random
website so there is no structure in that
but we still have to find the inside out
of it right so it's all these things
return allocates business analytics and
data science is this has to be true in
both structure and on sadita right and
that's what we will also push in the way
we start with the structure latent I
draw and cover the insights and then at
the latest text and image data and
process it and how to build the models
around and how to actually take out the
unstructured data as well right so these
are the two things that I'll post here
and I will try to understand anything go
ahead any questions
okay so I dated it as in Gnome so head
we already spoke about the skills right
and to do that though there are certain
tools which are required and with the
London Eye and I mentioned for the tools
here so they're not going to learn and
everything here but the important are
tools that we should know our the truth
are his face is here at the Python and
basically tab you right SAS was oh it's
kind of outdated so no one will know
it's fast now the important choose that
we should we will be done and then week
two in the market are SQL it
to transform others as told cleaning all
these things and so can you at least
comment there are a lot of yeah
thank you so yeah these are the tools
that we will be learning in the sessions
right our finest human de yeah just move
ahead so yeah and we see that the Python
is is taking interaction and it's the
users are increasing yep I J so will be
that's why it is given to but right so
that's that's on the skills and the data
of sorry dual set of things before again
moving ahead
any questions till now so just a couple
of minutes on data science process so
so if not so basically every data
science problem
it's it comes with the process randomly
jumping into things so there is this
structured part that we should take to
end up solving it in a science problem
so it starts with again as I already
mentioned the business knowledge is is
really important so that's where you
bring in your business knowledge and
data science knowledge and both come
together and try to define and translate
that problem into a data science problem
so you have to this if you are a
business person and you have to take
take a decision on what problem you
solve right so maybe I want to solve
mature problem people leaving from my
company I want to know why from my
authorizes my company or my platform and
that's the problem set and their spirit
simple right and you want to convert
this into a translate this into a data
science problem and how can I skeptical
Kanaka start thinking around how can I
solve it and basically then you bring
your machine knowledge okay I want to
use first regression maybe then I can
build a churn model and maybe I can get
to know the reasons so you sort of try
to click around the problem and sort of
create the approach before actually
diving into the solution itself once you
have an approaching place and you start
collecting the data to sort of solve
that problem right you know what all
features that is going to affect your
problem for example for the chore
problem the 100 is its right maybe you
as quietiy is not good or service is not
true there can be anything so you sort
of start collecting the data and
and as he mentioned kind of pre-process
it as well so to make sure that you get
to the right variations for that it can
be used in a further steps and once the
cleaning or the briefing is done we will
do something called as feature
engineering right so it's it's it's
basically creating new features or media
bills from the existing datasets right
again
they're one of things in that we will be
looking in the we'll be looking at them
in classes but again this this at a high
level
I'm just looking you guys too once that
is done we will look into building
algorithms right so that's what your
machine learning pieces what are the
different machine learning algorithms
what can be used for this program
statement and how to tune them and what
are the valuation steps towards the
accuracy and is it is it good enough to
go to the production and all these
things because you will be in the model
building process and at the end and what
the mattress most is the in science for
insights I'm able to get from that this
particular problem and can I just lower
and deployment deploy tilde you know
whatever floods and pressures if it's a
recommendation system
this robot go back and you plot in the
sunshine and so that you can see all
recommendations in the front end right
so these are the certain problems that
that can be mean this is sort of process
that we follow to make sure that we are
from a fine fact right so we will
definitely discuss a couple of
applications so that you will get an
idea on whatever how to do all these
things right so yeah
again before moving the heads of the
opportunities piece it has anyone anyone
has it has any questions okay fine I
think there's a notes so again coming
back to the opportunities part we see
that as we saw in the first slide there
are a lot of data is generating and the
same exaggeration a lot of jobs also in
amateur right so as long as the data is
pleasing we need to get insights out of
it and need more people who do it so
that's why we need more data scientists
right so it's it's very simple
calculation so that's why we have no
opportunities around and it assigns
because the data is wrong right a lot of
studies says that we are we are really
short of data science and analytic
skills and this is how it looks like in
2021 we we are we are all change what is
the actually the industry is demanded
night so that's why everyone is stepping
into this direction and maybe kind of
upscale and it's their kid right so yeah
so we sort of the roles that there are
different roles that right need a
scientist operationalize divine genius
system analyzed data mining analyst
product managers
Financial Analysts right for the data
analyze dimensional so these are the
roles different roles that are present
right now fairly the skills that
required almost remains same for all
these routes but there are slight
difference that's why we have our two
main roles in place but we can call them
as data speed in short and to make to
make sure that they are not lost in your
mouth right and yeah so yeah we still
see that a gap is and here to grow it's
not only the opportunities and jobs it's
it's also the skill gap as there are lot
of people getting into later kind but
they are not getting into
they're not getting to know the things
that are actually required they just buy
but that's not fairly enough right so
you need to have the skills which are
required so that the skill gap is filled
so you need to know whatever we
discussed earlier write this at this
stage machine learning NLP handling text
data animation so you sort of should
know everything and on top of that
knowledge we will make computer
scientist otherwise the skill gap is is
really a huge problem right so that sort
of coming from the Portuguese side of
things in short there are lot of meat of
course it is out there but the problem
is no one is really mean still there are
lot interviewing lot of people and they
are not getting hired because of the
same countries and
they don't know all the aspects or all
the things that a scientist should know
so I would highly recommend to get to
know all things and then go and either
introduce it it's really easy to easy
it's really easy after that but that's
that sort of trend is being without
knowing things should not go and imagine
things right so that's from the
opportunities side of things so again
I'd take a pause here and open for
questions
you
there's more questions is everyone there
in the goal okay can someone fly me
please
Santosh what you know I don't know any
doubt sir
okay okay yeah please please let me know
if you have any questions at any point
of time
okay okay so what we'll do
we'll just quickly discuss a couple of
complication right so I believe and
again last few minutes and open for
questions any questions you can answer
you can ask me related and literally
defines and I try to answer right so
yeah before that before I go to that
session we will just quickly see couple
of applications of data science so that
you know what you're getting into right
so these are sort of things that you
will be solving when you actually get
into the data science Viloria business -
and you should be knowing all these
things but you don't know what exactly
happening the Mac in or you know that
okay there is an AI I'm right but you
should be already going on this
application
right so example to Facebook taxation so
if you have observed if you let's say
you upload a facebook image you can see
that it will automatically suggest you
to tag a person it says okay can can I
drag this person or this name right so
it's basically a facial recognition
system that that runs in the back on the
Facebook it detects and it classifies
this image
to a certain phase right and it will
show okay can i attack this right and
netflix recommendation I think it's all
in popular it's more popular than
anything if you there was a competition
and popular combination system came into
picture right so all all of this the
whole reputation for is is running
behind the back end is is basically a
similarity matrix machine or mean deep
commission and sorry a natural language
processing where you basically compare
the text and find this malarkey on these
kind of things right so our Netflix
Amazon the recommendation systems are
purely based on an s-rank processing and
machine learning and certain statistical
measures right so that's another
application and also for example flood
detection right this is a biggest
problem in banking and finance industry
and they're using machine learning
algorithms to detect whether a
particular customer is a fraud or not
right so it's a classification problem
so there are two kinds of problem
regression and classification for using
continuous wave to predict the new
submission otherwise classification we
get to know in the film classes but yeah
you one of the equation is the fraud
detection where you basically predict
that this customer is a fraud of not
right
and also similarly spam filter spam
classification with are you you observe
that you predict that particular
incoming email is spam and that
particular spam ming-lee
would with an folder if you have
observed go to see name there is a and
folder and the lot of power means there
right and if you observe closely those
are actually unnecessary spam means and
that's being that they
is a natural language processing
behavior which will basically classifies
your textiles whether it's a spam on
hand right so this exact to use case
we'll be solving in in the classes array
the coming classes right and also the
segmentation when the web search is out
of its ancestry the right and also shown
analysis this I was explaining right oh
you any every organization wants to know
why people are leaving from their
company right
for example a chart shown analytics
would when a certain employee is this
going to leave the organization and why
he is right so this sort of gives is
idea okay this is leaving next month
activity so I should do something about
it right so and of course the cells
doing card we will not be exactly doing
said training per se but this is one of
the applications of AI and science right
so we will just explanations for
everything most be the same things right
so there is a smart speaker which is
generate emotions as well right so the
sentiment analysis is the biggest thing
when you read a text data or you process
it takes data and process an audio and
try to find out what is the sentiment of
noise right so this is sentiment
analysis is the place they're really
useful to understand sentiment of a
particular product for example you
release a new product in Amazon and you
want to know how to be you star and if
not be each and every view right so you
basically scrape all the reviews of the
fixator you sort of process it clean it
and you push it to a sketch image
analysis or package and you basically
find out the pets
and of the oral sentiment of that
product okay
you will get to know to the positive and
negative things for example if it's a
mobile phone okay battery is good camera
is good but my screen is not good I am
suddenly that so those those are the
things that that can be done right and
there is there's a big boom in the
commission industry as well and so
trying to automate things a certain
human being is doing example the lawyer
would so you've changed the machines you
sort of ask any question legal questions
to that and it it's it should be able to
answer so you're kind of richly moving
the lawyer is from from your life right
so there is visual analytics as well
just of course this the superhuman
structure there it's it's a lot of
studies shows that the error that made
from a human being or a human doctor is
more than error made by a machine to
read an x-ray of a particular disease so
you have a disease you will have you
will have results for ask and in
deserves x-ray serves and the human
being might might miss one or two things
and that caused problems but we see that
machines are doing far better than
humans are at this time right and it's
still the beginning a lot of things to
come at so again of course Don said
drying us right so this is a few
examples that I am just putting so that
you get an idea of what how how the
things to looks like when you are
solving certain problems so in this we
will be using we will be doing across
case studies
because of the few things from here and
we'll be implementing into n as entrant
projects for example product kit
recommendation system or product
category classification or sentiment
analysis of protection so we'll be doing
certain projects as capstone projects
and being able to understand how the
flow was exactly are these things are
being done in a back-end right so yeah
maybe I was asked at least got an idea
what all things how things will be done
so I will pause here and I will be open
for questions any guys Ashok
okay sorry I miss the questions here
yeah
one question is can you do this all
things in cloud also yes we can do all
these things in cloud maybe to start off
with we will be learning in the local so
that will not make things complicated
but yes of course we can't do everything
and cloud and to answer the one more
question can you tell me the syllabus we
will cover yes of course
I think it's already mentioned and other
sites that's what we'll be covering but
just quickly walk you through what are
the things that we will just a second
okay so I enough explain the flow out
here
so but again to reiterate I will be
covering the chores like art and of SQL
orange my turn
and statistics or which switch which is
a really important component and we will
be covering in stem databases and data
science interruption to data science
what are the things which are present a
statistical inference of course Python
and machine learning all the Machine
algorithms and of course supervised
learning is part of machine learning and
also on supervised learning where it's
sort of how to do which is clustering
and you see all those things or the
different types of unsupervised best
kirstine and of course recommendation
systems and also the inhale peanuts
language processing how to deal with the
text data rights for example sentiment
analysis and span and classification so
we will be covering a NLP as well and we
also will be covering tea planning so
what is a convolutional neural network
or physical in unit works how to solve
image problems how to do a text
summarization and all these things right
so this is the sort of the ancient
curriculum how it looks I think it will
be sent to you guys dollar mail it's if
it's not wrong but yeah we will be
learning all the things which are
basically required for data scientists
and from from from our point of view the
things that we'll be learning so I hope
does that answers your question
any other questions
are you providing any of the retail
product also yes yes we will be doing
our retail projects it's it's actually
present in go to the website and check I
don't know it's mentioned or not yeah we
will be doing the little heated projects
the e-commerce projects of healthcare
banking and four heads yeah yeah that's
that's all so yeah if you will be
discussing projects across domains
mostly into e-commerce banking I would
say and if you have specific
requirements that okay I need I need to
do a project on this industry we will go
ahead and facilitate that you may be you
may be Santos wants to do on banking and
someone else wants to do on retail it's
fine so we can say that Ratan doing
specific projects on incendiary don't
mind so yes
so does I think I think that access or
question any any other questions so
along the or duration course to complete
course okay so the duration of the
course will be close to four months is
so it will it will take around four
months to to sort of learn all these
things so it's it's not crap a sense of
course will be all the wicked and also a
lot of assignments will be given to Saul
and we need to cover all these things
right so it will it will be close to
three to four months course any other
questions
Thanks so if there are any questions
please let me know
so yep good sir are we frying or
intrusive classes are a practices that
you can ask servants chain but as far as
I know I think it's online class both
the classes are there we both gain
enough in classes it's your preference
but I think this particular demo was
along main session ideas
any other questions yes if you have any
questions please go ahead and ask the
trainer I think you don't have any more
questions so functional sir okay yes
what is the salary expectation from the
organization's than the course okay so
it depends on the experience so you're
asking in India right yeah so I depends
on your previous number of years
experience and what kind of company are
you getting into all those things but as
the market trends I just show you one
yeah
so as now the market says give you a
refresher or 0-3 experience so six to
seven taxes is what the salary the
companies are giving right and three to
seven years leavened X and 7 to 10 21
and it goes on so the the basics every
is somewhere around six five to six
lakhs for data science knows hello yes
yeah actually we are working experience
exhales questions oh you don't have to
apply as a fresher why because you have
four years of telecom knowledge right
illa from industry knowledge and your
important piece here is the business
knowledge also so you can continue as
the business knowledge and you can learn
all these things and you can go ahead
and apply for any other telecom industry
or you can do certain process and you
can showcase as that you have the
exposure to these things so you would be
considered as a stretcher or parsley so
you will be you will be in a next role
itself so it is answer in short no yeah
yes yes that can repeat the last plane I
missed it
okay in short no oh okay
so yeah
