hi how does it feel when someone you
like on tinder likes you back great
isn't it especially with guys does it
happen that the first person you like
likes you back second third thousand
millionth God knows what's that number
but then don't you figure out how to
crack the system what kind of person
will like you back and what kind of
person will not like you isn't it how do
you do that how do you come to that
conclusion there are multiple data
points of past and of expectations and
then you make the decision you
understand the scenario have you learnt
cycling or swimming isn't it a great
feeling to swim or cycle once you've
learned it it's fun isn't it but was it
easy to learn it initially you had no
idea how to balance yourself how to keep
yourself afloat people tell you oh keep
your legs straight don't bend it only
then you'll be able to float it but then
then you forget it but then you practice
and then you realize oh yeah it does
help me
oh this trick works better oh that's how
I have to balance myself in the cycle or
I'll fall down there multiple negative
rewards of falling and then there's the
ultimate positive reward and in between
small positive rewards when you balance
yourself for 10 meter 20 meter and so on
and so forth what are you doing over
here in your mind subconsciously you're
gathering multiple data and then
connecting those dots and figuring out
how to bicycle how to swim that's how
one learn anything isn't it so this is
how decisions are made yeah this is what
a data scientist is who makes decision
based on multiple data depending on what
positive rewards are and what the
negative rewards are companies like
individuals have to make decisions to
survive in the market to optimize their
existence to optimize their revenue
their profit and hence they have
they have to have data scientists to
make to help them make those decisions
to get those actionable insights pause
the video for a moment and think what
kind of decisions do companies make what
kind of companies can you think of what
are those companies which are new what
kind of decisions they make what are the
companies that are old which have always
existed much before you were born what
kind of decisions are they making what
kind of data they need what kind of
decision they make is helped by the data
that they have
what kind of companies did you think of
what are the old companies such as the
oil companies the railways what are the
new companies like Netflix Amazon Quora
Facebook YouTube and of course our
tinder did you hear of the news recently
the Saudi oil company Aramco there was
an attack on its oil fields it was a big
thing five percent of world's crude oil
production just came down they have to
re optimize everything what do they do
they produce oil they transport it from
one place to another from the crude oil
to the refinery the various parts the
petrol diesel the aircraft fuel kerosene
wax and they have to transport it to
different customers across the world
from America to Australia from China to
Chile how do they do it they have to get
it to the port they have certain
vehicles so they they have to follow
something called the vehicle routing
problem I have multiple tankers which
have certain capacity my manufacturing
is happening somewhere my ports are
somewhere I have to get these from here
to there the tankers are there and there
are multiple ports with multiple demands
different demands and multiples supply
points what happens after that then it
moves to different companies then
the companies like musk come in it's
also very old company they have to ship
goods from one place to another they
have to decide what capacity ship to
keep in which location call it vehicle
routing problem call it Travelling
Salesman problem the capacity problem
the knapsack problem these are all
operations research problem they also
have to figure out you know if the good
is perishable if they are transporting
vegetables or fruits from one place to
another they have to do it in stipulated
time there's a cost involved there's a
lot of data involved how to make those
decisions what are what about the new
age companies how does Netflix decide
which is the show that you would like
you keep watching certain shows they're
people who watch similar shows and they
have like certain kind of shows would
you also like that show or you won't
like it
if you're on Amazon what kind of clothes
will you buy will those clothes look
good on you the clothes that you like
are the clothes that people like on you
like your Instagram photos
how does Instagram figure out what
things you would like to follow what
information you would like to know these
are all data science questions in this
new age things have evolved the data
science has become an extremely
important field for business to survive
was it always the case
yeah the answer is no but why not
what is it new that has come which was
not before is it just the new age
company or our way of looking at the
problems are we looking at things
differently which we could not do
earlier were they are not smart people
before there was Newton there was
Galileo there was Einstein what is there
in data science which was not there
before and has come in
give it a try what are the components of
data science business you know company
like shell has existed long before this
data science came into existence were
they not doing it before? you as an
individual don't do it before? what has
changed
give the video a pause again and think
there are different aspects of data
science there is statistics machine
learning the whole business
understanding deep learning you know
lots of data visualization you know
these are the words you keep hearing
here and there and everywhere but what
has changed really are two things one
the computational power - the cheapness
of storing the data they were the there
was a airline industry which was very
common after World War two starting
1950s but now those same airline
companies can store all the data of each
flight of every sensor on the flight
there are thousands of sensors on each
of the flight and they are thousands
millions of aircraft flying above at any
point of time and all that data is
stored not just for that moment but for
months and months and months behind
suppose that you are manager of a
railroad company a train system which
transports goods from the East Coast
somewhere near New York to the west
coast towards California you transport a
lot of shipment what is parity for you
that delivery should happen on time
which is the biggest risk that you have
that the trains derail there's a crack
on the railways so you keep auditing
those tracks that there's no crack and
the trains do not have to stop there
there are no problems in transporting
the goods but if you keep auditing the
tracks very often then that that would
delay the trains that would cost you if
you audit it after too much gap then
cracks may develop and there may be
train accidents so you have to optimize
that how do you do that you collect lots
and lots and lots of data of past when
accidents happen when did not happen
zero no accident very few ones accidents
did happen and you have to prevent them
you have you know billions of rows of
data and thousands of columns how many
trains have passed what was the
wait of those trains how fast the trains
were traveling what were the weather
conditions what was the temperature each
hour each minute each second the
temperature you have recorded it and
then you see with a concept a similar
simple statistical concept like logistic
regression Oh if these conditions are
met the cracks may develop and accidents
may happen if these are not happening
then certainly no accidents are
happening and there is a grey region in
between where you optimize your
surveillance and you improve your
business you minimize your cost how
could you do it they were railroad
companies before as well they were not
able to do it but now you can do it
because you can store terabytes of data
at a very cheap cost and do computation
of them very fast and very easily we
will ask three questions now first what
is a data scientist second who should
become a data scientist third how to
become a data scientist let's start with
the first one what is the data scientist
what do you think it's a very simple
answer data scientist is a person who
helps the business answer relevant
questions the help the business get
actionable insights which helps the
business grow increase its revenue
minimize its cost optimize its profit
how do they do it they do it using data
many people confuse data scientist as
being someone who learns lot of
statistics machine learning is a geek
and this and that it's the end that
defines the means and not the other way
around what are the problems that we are
solving what are we doing a data
scientist answers business questions not
only that he helps business ask
questions or she uses data in such a way
that the data makes you ask questions or
gives you insight oh is it
I never thought so
let me give an example how does Apple
the site when to launch a new mount
model you know if it launches too soon
it will cannibalize the sales of its
previous model if it launches too late
then Samsung or Nokia on motorola would
come up with a new model and eat away
Apple's market so they have to optimize
it in some cases it can be a simple
trend line which shows that you know my
sales of the previous models is still
increasing and this is not a point to
launch a new model or it has become flat
I'm still going to get business this is
not a point to launch a new model or
it's coming down maybe you know when it
starts coming down or when it reaches a
certain point but is that only so no you
have to see Samsung you have to see
Motorola what are they doing after how
much gap do they usually launch a new
model
oh my sales are going up but there's a
very high likelihood that Nokia is going
to launch a new model and before they
launch it I have to launch a new one
this you know can be done just using
visualization though it's not as simple
as it sounds like but there are many
business problems that are solved like
that so this is what who a data
scientist is so what was the second
question
yeah it was who should become a data
scientist what do you think my answer
would be a person who wants to make
sense of the world around himself or
herself should become a data scientist
pick up the data from a store or across
all the stores of Walmart and see what
things people buy together depending on
their age depending on the agenda you
know on Friday people buy nappies and
beer together why does that happen
would you ever thought that data threw
it out for you by a simple concept
called Market Basket analysis or what is
the buying pattern different based on
the gender you know is it so that
females in America and female
in China have the same buying pattern as
compared to men from China and America
or is it different yeah
these things happen data throws you all
this as a data scientist these are the
interesting things that you would come
to know and then you'll have your aha
moment oh I see I never thought that
that was so counterintuitive but once
you realize it wow that was fun that was
amazing
let me meet a friend and talk to him
about it or our friends or let me put a
youtube video on it you know life is
very exciting as a data scientist it's
it's not just mundane work of coding you
know many people like coding it's not
mundane for most of the people but for
some it is but then all the things
doesn't matter Whether I'll be able to
code whether I'll be able to
understand statistics or not know once
you find that interest you know which
everyone finds to make sense of the
world around themselves then all these
things would become trivial and for
these you know there are tons of people
there to help you like me and our
company and we'll do that so that brings
us to the final question the third
question how to become a data scientist
you know this is a very fast evolving
space there are multiple aspects new things
come old things go technologies come and
go but the overarching thing that you
have to understand is to make sense of
things to get an idea and no one no one
can know all the things different people
know different things and people work
together and understand you can be a
visualization expert you can be an ETL
expert you can be a machine learning
deep learning reinforcement learning
expert or you may just know how to code
as long as you're keen to understand
trying to figure out things would happen
so certain basic things are there like
if you want to go the coding way and if
you don't have much of an experience
then our is a very good language to pick
up there's a very good language called
Python as well the certain analysis that
can be done in SQL as well or in Excel
as well arranging the data pivoting them
taking month wise data like this and you
know just holding their ear here and
turning them around turning
them like the slicing dicing putting
graphs does it require a line graph
the import of United States of
America based on the continents that
would not require line graph that would
require a pie chart simple visualization
ideas and then how to do it in
matplotlib in Python or ggplot in R or
or in any other or in tableau you know
so these things are there then the most
important thing is understanding the
business what business wants what kind
of questions they are asking what
insights that they are looking for how
to model them suppose you're running a
hospital so how to understand the
distribution of arrival of people it can
be passed on distribution and then
figuring out how much time it takes for
a person's x-ray to happen that can be
uniform distribution how much time does
a doctor spend with a person that can be
exponential distribution basic small
statistics concept which if taught
properly can be very easily understood
so there is programming there is
statistics there is business modeling
and of course the whole data engineering
aspect of storing the data in what
format to store it so that the retrieval
is easy whether I should use Hadoop or
spark
what is MapReduce so these are the
different things that a data scientist
can know and keep in mind one does not
have to know everything you don't have
think oh my god so much to study how
would I do it you know I'll have to
learn everything that would take me ages
no it doesn't happen like that six
months is good enough to know a lot of
things and you can start you know sub by
a programming language immediately or by
learning statistics immediately no one
in this industry knows everything they
work with each other they have a little
idea of what the others are doing they
can with time pick up what others are
doing bits and pieces of it but then you
know no one has to do everything so you
don't have to worry about it and if
there's even a small bit of worry how to
understand thing then we are over here
to help you learn it you choose which
one you want you want to start with
Python you want to start with statistics
or you want us to
you decide where to start how to package
it we are there for you
we at Lecteron have committed data
scientist from different industrial
background with different knowledge with
different academic backgrounds some are
PhDs some are masters and statistics
some are masters in programming some a
PhD in computer science and based on
their experience some work in academics
some keep evolving different algorithm
for business and based on their
experience and knowledge we have created
bit-sized courses for you and we have
tested them Lecteron dust testing at its
own campus and tries to figure out
whether which is the best way people are
learning and you also figure out that
different people have different learning
style so we have created those courses
different aspects different styles
different modules so that your learning
is most easy and most impactful they
have multiple tests certifications to
keep you on your toes
and keep learning as fast as possible
and as much as possible let's all learn
together and everyone be more
knowledgeable hey my phone is buzzing
gosh my wife's message she's asking
whether I'm on tinder I was just joking
I'm not there on tinder got to go hey
don't forget to subscribe the channel
below see ya bye bye
