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 program in big data and data science so
if you would like to become an expert in data science or big data you can check
out our master certification training courses which are big data data signed
certification master course and data science master course and big data
architect master course and with this we 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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