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Amity Institute of Information Technology
Introduction to Data Science
BSc.IT/BCA/ DUAL VI Semester
Faculty: Dr. Shambhu Kumar Jha
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Module I
Introduction to Big Data
Difference between Big Data and Data Science,
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let’s start by understanding what is Big Data?
? Big Data: It is large or voluminous data, information,
or the relevant statistics acquired by large
organizations and ventures from various sources.
? Many software and data storages is created and
prepared as it is difficult to compute the big data
manually.
? It is used to discover patterns and trends and make
decisions related to human behavior and interaction
technology
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Big data encompasses following wide variety of data types:
? Structured data, such as transactions and financial records;
? Unstructured data, such as email, text, documents and multimedia
files;
? Semi structured data, such as web server logs and streaming data
from sensors.
Big data is often characterized by the three V's:
? Large volume of data in many environments;
? Variety of data types frequently stored in big data systems; and
? Velocity at which much of the data is generated, collected and
processed. 4

Why Big Data is Important
Companies use big data to :
? Improve operations,
? Provide better customer service,
? Create personalized marketing campaigns and take other actions to
increase revenue and profits.
? Competitive advantage over those that don't because they're able to
make faster and more informed business decisions.
? Example: Big data provides valuable insights into customers that
companies can use to refine their marketing, advertising and
promotions in order to increase customer engagement and conversion
rates.
? Both historical and real-time data can be analyzed to assess the
evolving preferences of consumers or corporate buyers, enabling
businesses to become more responsive to customer wants and needs.5

Is big data part of data science?
? Big Data is essentially a special application of data science, in
which the data sets are enormous and require overcoming logistical
challenges to deal with them.
? The primary concern is efficiently capturing, storing, extracting,
processing, and analyzing information from these enormous data
sets.
? Big data is a combination of structured, semi structured and
unstructured data collected by organizations that can be mined for
information and used in machine learning projects, predictive
modeling and other advanced analytics applications.
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Differences between Big Data and Data
Science:
DATA SCIENCE BIG DATA
It is about the collection,
processing, analysing, and utilizing
of data in various operations.
It is about extracting vital and valuable
information from a huge amount of data.
It is a field of study just like
Computer Science, Applied
Statistics, or Applied Mathematics.
It is a technique for tracking and
discovering trends in complex data sets.
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Science:
The goal is to build data-dominant
products for a venture.
The goal is to make data more vital and
usable i.e. by extracting only important
information from the huge data within
existing traditional aspects.
Tools mainly used in Data Science
include SAS, R, Python, etc
Tools mostly used in Big Data include
Hadoop, Spark, Flink, etc.
It is a superset of Big Data as data
science consists of Data scrapping,
cleaning, visualization, statistics, and
many more techniques.
It is a sub-set of Data Science as mining
activities which is in a pipeline of Data
science.
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Science:
It is mainly used for scientific purposes.
It is mainly used for business purposes and
customer satisfaction.
It broadly focuses on the science of the data.
It is more involved with the processes of handling
voluminous data.
It is mainly used for scientific purposes.
It is mainly used for business purposes and
customer satisfaction.
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AMITY INSTITUTE OF INFORMATION TECHNOLOGY
Applications Of Big Data Finance
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Applications of Big Data: Social Network
?Social media in the current scenario is
considered as the largest data generator.
?The stats have shown that around 500+
terabytes of new data get generated into the
databases of social media every day, particularly
in the case of Facebook.

Applications of Big Data: Healthcare
?Nowadays, doctors rely mostly on patients’
clinical records, which means that a lot of data
needs to be gathered, that too for different
patients.
?Since there is a large amount of data coming
from different sources, in various formats, the
need to handle this large amount of data is
increased

Applications of Big Data E-Commerce
?Maintaining customer relationships is the most important in the e-
commerce industry.
?E-commerce websites have different marketing ideas to retail their
merchandise to their customers, to manage transactions, and to
implement better tactics of using innovative ideas with Big Data to
improve businesses.

Applications of Big Data: Education
The education sector holds a lot of information with regard to curriculum,
students, and faculty.
The information is analyzed to get insights that can enhance the operational
adequacy of the educational organization.
Collecting and analyzing information of a student such as attendance, test scores,
grades, and other issues take up a lot of data.
So, big data makes an approach for a progressive framework wherein this data
can be stored and analyzed making it easier for the institutes to work with.

Application of Big data
Big Data in Communications
?Gaining new subscribers, retaining customers, and expanding
within current subscriber bases are top priorities for
telecommunication service providers.
?The solutions to these challenges lie in the ability to combine
and analyze the masses of customer-generated data and
machine-generated data that is being created every day.
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Skills Required Becoming a Data Scientist
? In-depth knowledge of SAS or R. For data science, R is generally
preferred.
? Python coding: Python is the most common coding language that is
used in data science, along with Java, Perl, and C/C++.
? Hadoop platform: Although not always a requirement, knowing the
Hadoop platform is still preferred for the field. Having some
experience in Hive or Pig is also beneficial.
? SQL database/coding: Although NoSQL and Hadoop have become a
significant part of data science, it is still preferred if you can write
and execute complex queries in SQL.
? Working with unstructured data: It is essential that a data
scientist can work with unstructured data, whether on social media,
video feeds, or audio.
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Skills Required Becoming a Big Data Specialist
? Analytical skills: These skills are essential for making sense of data,
and determining which data is relevant when creating reports and
looking for solutions.
? Creativity: You need to have the ability to create new methods to
gather, interpret, and analyze a data strategy. Mathematics and
statistical skills: Good, old-fashioned “number crunching” is also
necessary, be it in data science, data analytics, or big data.
? Computer science: Computers are the backbone of every data strategy.
Programmers will have a constant need to come up with algorithms to
process data into insights.
? Business skills: Big data professionals will need to have an
understanding of the business objectives that are in place, as well as
the underlying processes that drive the growth of the business and its
profits.
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L3 Big Data and Application.pptx

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