Welcome to the world of data. So data
today is growing faster than ever before
which makes it important for us to know
the basics of the domain like data
science, big data and data analytics. So
most of the people are actually being
confused amongst these terms. So in the
session I'll talk about the distinction
between data science, big data and data analytics based on what is it, where it is used.
You will also look at the roles and
responsibilities to become professionals
in the field with their skills and
the salary prospects in each field and then
we'll take the example of Amazon to see
their respective job responsibilities. So
let's begin with understanding the basic
concepts of these. So big data is huge
volumes of data that can be of
structured, semi structured and
unstructured and they are generated in
multi terabytes through various digital
channels like mobile, internet and social
media etc and these are not able to be
processed using traditional applications.
So now unlike traditional technologies
like RDBMS, Big Data actually
processes large volume of data at a
faster pace and also provides you an
opportunity to store the data with
different tools, technologies and
methodology. Now Big Data solutions
actually provide the techniques to
capture, store and analyze even search
the data in seconds that make it easy to
find insights and relationships for
innovation and competitive game. So with
suitable analytics, big data can be used
to determine the causes of business
failure, cost reduction, time-saving,
better decision making and new product
creation. So individual with knowledge of
Big Data are referred to as Big Data Specialist and hence Big Data Specialist
will have expertise in let's
say Hadoop, Mapreduce, Spark, NO SQL and DB
tools like HBase, Cassandra and MongoDB
etc so data science actually tackles big
data to extract information. So it's a
field which is embracing all that
is associated with structured and
unstructured data starting with
preparing, cleansing, analyzing and
deriving useful insights and again it's
a combination of mathematics, statistics,
intelligent data capture
programming etc so in a nutshell it's a
combination of several techniques and
processes working on big churns of data to
gain knowledgeable business insights. So
they would initially gather data sets
from distinct disciplines and then
compile it and after compilation they
apply predictive analysis, machine
learning and sentiment analysis. So
finally data scientists would actually
extract some useful information from it.
Now data scientists understand data in a
business view and provide accurate
prediction and charges for the same and
thus preventing a business person from
future loss. So data scientists will have
expertise in let's say statistics, logistics and linear regression
differential and integral calculus among
other mathematical techniques.  Now you
could also use tools like R, Python, Sas, SQL, tableau and so on. So most of us are of
the opinion that both data science and
data analytics are similar which is not
the case. Yes they both actually differ
at some minute point and that can be
noticed through deep concentration. Now
data analytics is the fundamental level
of data science and you need to know
this so data analytics makes use of data
mining and techniques and tools to
discover patterns in the analyzed data
set. So here we actually are mainly
looking into the historical data from a
completely modern perspective and
applying methodologies to find a better
solution. Now not only this but data
analytics would also predict the
upcoming opportunities which company can
exploit. So data science actually
utilizes data analytics to provide
strategic and actionable insights. So
here data analyst plays a major role. So
he'll have expertise in let's say R
statistical computing, data mining
techniques, data visualization
and python programming. Now we look at
some of the applications of each. So the
retail industry, they also use big data
to remain in the retail business and
stay in competitive. So the important
key here is to understand and solve the
customer better. So this would actually
require proper analysis of all the
sources of different data just like data
from customer transaction, web locks
loyalty program data, social media data
and so on and this can be easily done
with big data. Now we all know that
telecommunication service providers have
priorities of retaining customers,
gaining new ones and expanding the
current customer bases. Now so in order
to do this the act of combining and
analyzing terms of customer and machine
generated data created on a daily basis
can be done with big data.
Now even big financial service providing
forms just like retail banks, credit card
companies, insurance firms, venture funds
etc, they also make use of big data for
their financial services. So the major
challenge experienced by all of them is
the large amount of multi structured
data embedded in multiple different
systems and now this can only be taken
care of by Big Data. So big data is
actually use in various ways such as
fraud analytics, customer analytics,
operational analytics and compliance
analytics. Now while data science has its
own heights one of the most common
application is recommender systems. Yes
so these system adds so much to user
experience and also make it easy for
users to find relevant recommendations
and choices of their interest. Now it can
be anything like relevant job postings,
movies of interest, suggested videos,
Facebook friends or people who bought
this also bought this etc. So several
companies actually are using this
recommender systems for promoting their
suggestions and products according to
the users interest and relevance of
information and demands. So
recommendations always depend upon the
previous search result of users. Now
another one is internet search.
So here many search engines use data
science algorithms to deliver the best
results in just a split of second. And
then the whole digital marketing
ecosystem makes use of data science
algorithm and that is the major reason
why digital ads get higher CTR then the
conventional forms of advertisements.
Let me tell you guys that data science
applications are not limited to these.
Yes it can be implemented on web
development, ecommerce, finance, telecom
etc now on the other hand data analytics
for healthcare. Let's check it out. So the
major challenge today hospital are
facing is the cost pressure that needs
to be overcome to treat their patients
effectively and here machine and
instrument data is used increasingly for
tracking and optimizing treatment. Then
in the terms of gaming. So the advantage
analytics plays a major role over here
including collection of data in order to
optimize and spend across games. So
companies which are developing these
games get a good insight into likes,
dislikes and the relationships with
their users. And then let's suppose
travel industry. So again data analytics
is able to optimize the buying
experience through the mobile and the
social media. Travel sites can gain
insights into the customers desires and
preferences. So products can actually be
up sold by correlating the current sales
to the subsequent increase in browsing
habit and then personalized travel
recommendations can also be delivered by
data analytics based on social media
data now let us look at some of the
important roles and responsibilities in
each area so a big data specialist is a
professional who ensures uninterrupted
flow of data between servers and
applications so they actually work on
implementing conflicts big data projects
with the focus on collecting passing
managing analyzing and visualizing
larger sets of data to turn information
into insights right so they are actually
or they should be able to decide on the
needed hardware and software designs as
well now the big data engineer should be
able to
prototypes and proof of concepts for the
selected solutions bred as a data
scientist as a professional who uses
their technical and analytical
capabilities to extract meaningful
insight from data so they would actually
understand data from a business point of
view and it also been charge of making
predictions to help businesses take
accurate decisions so data scientists
come with a solid foundation of Computer
Applications modeling statistics and
math so they are again efficient in
picking the right problems which will
add again value to the organization
after resolving it and then if I talk
about DTI analysts then they also play a
major role in data science so they
perform a variety of tasks related to
collecting organizing data and obtaining
statistical information out of them so
they're also responsible to present the
data in form of charts graphs and tables
and then use the same to build
relational databases for the
organization now we look at some of the
skill sets that are required to be a
professional in this area so if you plan
or if you are planning to be a
professional and maintain town then you
should have mathematics and statistical
skills so that's very necessary for all
areas of data which includes big data
data science and into analytics after
all this is where job begins right and
then you also need to have analytical
skills so that is the ability to make
meaning out of tons of data and then as
computers are the engines that power
everyday data strategy and hence
computer science or computer sense skill
is the most important for a big data
professional and you also need to be
able to creatively put new methods
together for gathering interpreting and
analyzing data and after that if you
want to be a data scientist then you
must be able to work with unstructured
data which is very important and
irrespective of wedge comes from I mean
whether it's from audio social media or
video feeds and then you should also
need to have good knowledge of Hadoop
platform and with that it is also an
added advantage if you know coding and
byte
because fightin is known to be the most
common coding language used in data
science apart from Perl Java C C++ etc
now you can also have deep knowledge of
our ourselves because our programming is
another preferable programming language
and data science and let me tell you
guys that although Hadoop and no SQL are
major parts of data science but again
knowing how to write and execute complex
queries in SQL is again preferable and
then you'll need to know business skills
to get a good understanding of various
business objectives which pushes the
business to grow along with its profit
and if you want to become a data analyst
then you need to have a very good
knowledge of programming languages such
as Python and art because they are
really important in this field and then
as an aspiring data analyst statistical
skills and mathematics as they much
needed yes and again to be a data
analyst you need to map out and convert
raw data into another format that will
make it more convenient for consumption
and then with good communication and
data visualization skills again as a
must required and you must have data
intuition which means you need to think
and reason like a data analyst so these
were sort of prerequisites that you
actually should have if you want to
build your career into this respective
domains and then devote profiles of all
the three are entirely different yes
which makes their salaries to vary from
one another as well so let's discuss
that now so data science is booming like
anything and that is why it makes data
science to stand up at the top when it
comes to salary that is around one
hundred and twenty two thousand dollars
per year now next are the big data
specialists who can earn around one
hundred and fifteen thousand dollars per
year followed by the data analyst with
an annual income of ninety two thousand
dollars per year now we have come to a
point where we are going to discuss an
example of Amazon to understand how each
of them are related and providing its
benefits so let's begin with big data
so here the
huge amount of unstructured data is
being generated from various sources now
which is difficult to process through
traditional databases right so due to
this a Big Data profession creates an
environment using various big data
ecosystem tools to store and process
data effectively and timely now let's
see what is the role of data scientists
in Amazon example so here we are going
to talk about how Amazon optimizes its
business using data science so data
scientist is the one will be able to
drive sales with intentions product
recommendations and then he'll also
predict the future revenue that each
customer will bring to your business in
a given period and also they would
predict how often they are likely to
make purchase and the average value of
each purchase with customer lifetime
value modelling now they would also
discover which customers are likely to
churn that is to say acquiring new
customers as well as maintaining
relationship with existing ones the data
scientist usually creates a model to
automatically extract useful information
from reviews and with this information
Amazon can efficiently maximize user
satisfaction by prioritizing product
updates that will have the greatest
positive impact
now we'll see what's the rule of data
analysts in Amazon example so here data
analyst is actually responsible for
supply chain management which includes
managing data for products right from
warehouse to the customer so Amazon also
uses data extensively to manage
inventory also it helps to optimize
transportation and pricing of delivery
now data analysts will also be involved
in user experience analytics mainly
includes how is product search across
portfolio or vote decides the ranking
order of products for a particular
search or what is the best landing page
for a customer coming from a Facebook
etc Lindy diner list is also responsible
for let's say identifying merchant
customer fraud detection so this is how
Amazon leverages data science big data
and data analytics to make customer
experience a more delightful one now
that phenol the difference between the
three so which one do you think is the
most suitable for you where the option
is for you you can simply decide whether
you can make your current in data
science or big data or data analytics so
entire batch here has thousands of data
science big data and data analytics
course online including our integrated
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if you would like to become an expert in
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out our master certification training
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come to the end of this video I really
hope that by now you must have got a
clearer idea and a distinction between
all these terms and you must have got
what is actually the suitable courier
that can be for you so thank you so much
friends for giving us your precious time
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