Data Science

A blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from raw data.

Why data science?

Traditionally, the data that we had was mostly structured and small in size, which could be analysed by using simple BI tools. Unlike data in the traditional systems, which was mostly structured, today most of the data is unstructured or semi-structured.

This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analysing and drawing meaningful insights out of it.


Extract knowledge and insights from raw data 

Data science is a multidisciplinary field focused on finding actionable insights from large sets of raw and structured data.

Data science experts not only do exploratory analysis to discover insights from it, but also use several different techniques to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets in order to identify the occurrence of a particular event in the future.

A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

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Data science use cases

IT organisations need to address their complex and expanding data environments in order to identify new value sources, exploit opportunities, and grow or optimise themselves efficiently.

Data Science is disrupting the way we do business. Here are some examples of how dat science is being applied in various domains like Banking, Retail, Manufacturing, Transport, Healthcare, Ecommerce, etc.

Data Science info