Data Science is a multidisciplinary field which deals with Machine Learning, Mathematics,
Statistics, Data Modelling, Natural Language Processing and Statistics Modelling which helps
us in obtaining insightful information from the generated data. People, however, often get
confused about data analytics and data science. Data analytics deals with business
administration and exploratory data analysis while data science is concerned with Data
Product Engineering and Machine Learning and Advanced Algorithm. Data analytics and data
science are very closely related, but they are still different from each other. Data analytics is
one of the components of data sciences which helps in understanding the data produced by
an organization, whereas the output of data analytics is fed to data science to help solve the
problems.
Having known the difference between data analytics and data science, let us now see the
various components of data sciences.
Components of Data Science
Data science comprises of many fields which are pretty much related to each other and are
often used interchangeably with some differences. There are many components of data
science, but here we will talk about one of the major components which is Big Data.
Big Data
As the name suggests, Big Data refers to the high volume of structured and unstructured
data. Big Data has with time become the backbone of many large organizations. It helps in
taking better decisions and strategizing the business plans by providing information from the
generated data. In order to extract meaningful information from the data, Big Data breaks
down this huge data into 5 V’s:
A) Velocity - It refers to the rate at which this huge amount of data is being generated. So
what Big Data does is that it analyzes the data as soon as it is generated without storing it in
the databases.
B) Volume - It refers to the abundant data that is generated each second. So, in order to
store this huge data, distributed systems are used where data is stored in different locations
and brought together by the software.
C) Value - It refers to the worthiness of the extracted data. Big Data helps in understanding
the benefits of collecting and analyzing the data so that it can be monetized later on.
D) Variety - It refers to the different types of data that is being generated. Big Data
technology helps in storing and analyzing both structured as well as unstructured data.
E) Veracity - It refers to the trustworthiness, or the quality of the data. Big Data helps in
letting us know which data can be kept and which can be discarded.
In order to handle the huge amount of data, Big Data uses tools like Hadoop and Cloud
based Analytics, which are cost effective and provides efficient ways for handling the
business. Hadoop being a framework helps in developing open source software for highly
reliable, scalable and distributed computing. It distributes large data sets across different
computers and processes the information form the data separately by using simple
programming models thereby requiring thousands of machines instead of a single server.
Resource Box
Big Data is the need of the hour and with time, its importance is likely to increase as well. So
if you are doubtful of getting into this field, then worry not because you will be having a
great career once you enter this. Check out data science courses in Bangalore for more
information.
