Data Science vs Big Data vs Data Analytics

In today's world, data is growing at an unimaginable speed. It is all around us and has become a
part of our daily lives. Digital data is set to double every two years and has been shaping the
future world for us! With data growing in such incredible amounts, the issues arising with its
storage and processing have to be sorted. As data processing comes into picture, confusing
terms like Data Science vs Big Data vs Data Analytics come into play! This article is all about
sorting this confusion by briefing about each.
What is Data Science?
Data Science deals with structured and unstructured data by discovering their hidden patterns
with the help of various tools, algorithms and machine learning principles.
It involves problem-solving mechanisms and constructs new processes for data modeling and
production using prototypes, algorithms, predictive models and custom analysis that extracts
insights and information from data. In simple words, it cleanses, prepares and aligns data.
The Digital Marketing spectrum is totally dependent on Data Science for its digital
advertisements. Search engines run on data science algorithms to deliver personalized results
for search queries, enhancing the user experience by manifolds.
A Data Scientist performs data analysis to discover informational insights from the generated
data by using various advanced machine learning algorithms to identify the occurrence of a
particular event in the future.
These data make up extremely important business information.
What is Big Data?
As the name suggests, Big Data refers to huge volumes of data that can't be processed
effectively using traditional data processing applications within a given time and value. The
analysis of Big Data poses many challenges in sampling, capturing data, data storage, data
analysis, sharing, transfer, Querying, visualization etc. Big Data Analysis hence involves
predictive analytics, user behavior analytics, and other data analytics methods to extract
information from big data.
This information extracted from big data can be used for better decision making and strategic
business moves.
Big Data is used by governments, health managements, insurance firms, retail banks, credit
card companies and many other institutions which deal with huge amounts of data that is
difficult to store in one computer.
Big Data helps in gaining new subscribers or retaining customers by combining the data and
analyzing the masses of customer generated data and machine generated data that is being
created every second.
Some of its applications also includes analyzing weblogs, transaction data, customer
transaction data, social media, store brand credit card data and loyalty program data.
What is Data Analytics?
Data Analysis is used in Data cleansing, transforming and modelling data. It is the science of
examining raw data to reach certain conclusions about the information. With the help of data
analytics, we can discover useful information from the raw data to support our decision making
and business strategy. Data Analytics is also very helpful in identifying existing business
theories and models. If required, this technique verifies and disproves them.
Data Analytics can help optimize the purchasing experience for customers through weblog or
social media data analysis. With the help of data analytics, businesses can view insights into
customer's preferences. With the help of Data Analytics, companies can get a fair idea of what
their customer's likes and dislikes are.
Data Analytics is centered around controlling and monitoring of network devices and dispatch
crews. It also manages service outages. It integrates millions of generated data points in the
network performance and helps businesses to monitor the network.
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Comments

[maxmarrie]

I really appreciate you sharing this knowledge. I believe you should also include some details regarding platforms for data analytics similar to Einblick. Please share your thoughts on it. Thanks