Data Science - The Very Basics

We might have heard about this term quite often but unsure what exactly it is about. The term consists of two words. Data and Science. People who have little to no idea about Data Science must have guessed that it is something related to Data. Let’s break the bubble and get to know Data Science beyond its term.

First thing first, let us first understand what is Data Science?

An American journalist named Kenneth Neil Cukier said in a 2010 article in The Economist, data scientists “combine the skills of software programmer, statistician, and storyteller/artist to extract the nuggets of gold hidden under mountains of data.”

In simpler words, Data Science is a study using which we can get to know about hidden patterns, important information from raw data and applying those findings to make important business decisions. And you guessed it right, Data Science does deal with data, in fact, it deals with a lot of data.

The data used in Data Science can be both structured and unstructured. What does that mean? Structured data means the data stored in a clean and clear format such as your excel sheet, your CSV files etc.

Unstructured data means data that is not available in a presentable format but it’s rather scattered and you would need to find the data pieces from various sources.

There is a huge demand for a skilful data scientist but supply is not meeting the vast demand.

Why is a skilful data scientist so demanded? Well, the answer is there is an abundance of data available in one organization. These data are collected through various sources like social media, our footprints on the web and the internet of things etc.

But these data are mere numbers and of no use, if a data scientist does not see through it. As they are the people who will bring value out of it by bringing meaningful insights which will be the base of a business decision.

The various roles –

You must have heard about various job roles surrounded by the term data. Like Data architect, Data Engineer, Data Analyst, Statistician, Data manager etc. It all seems like the same job role but let me tell you it isn’t.

They are different jobs performing different roles, related to the same task goal and dependent on each other.

For example, to begin with, what we need is data itself. This data is extracted from different sources, integrated and presented by the Data Engineer to the Data Scientist who then utilises the data and adds value to it.

Application of Data Science in different industries –

Banking and Finance -:

Data science is helping the banking and financial sectors to detect fraudulent patterns, transactions to protect the hard-earned money by the depositors.

OTT platforms and recommendations -:

Over the top platforms use data science along with machine learning algorithms to recommend shows, movies, music, products based on your purchase pattern and watch list.

Healthcare -:

Data plays an important role for doctors to choose the safest and most worked treatments for their patients for better recovery results.

Logistics -:

For faster delivery, optimizing different routes, data science plays a huge role for logistics companies to increase their efficiency.

That is all for this article. I hope now a little bit of confusing cloud has been removed over the term Data Science. We will go in-depth in Data Science for detailed understanding in our coming articles. Stay tuned with us.

Thank you for investing your precious time in reading this article. Hope that you found this article helpful, if yes please share it with your circle.