With a growing number of internet connection points and a growing population, data is bound to become the new currency of a modern era.
Data in the 21st century is generated at a blithering rate. In fact, more data is generated in two days than was generated in the entire human history till 2003. Most of this data is unstructured in the form of videos, emails, transaction IDs, browsing history, etc. with quintillions of bytes being generated every day.
Proper structuring, cleaning and modelling of the most freely available currency in the world (you guessed it: data) is what is driving competition into finding new ways of dealing with such big chunks of information
In comes Data Science
Data science involves a conglomeration of exploratory data analysis, machine learning and data product engineering. Gaining fast traction in the past few years, its efficient and self-correcting (predictive) forms of data analysis give it an edge over orthodox forms of data handling.
Looking into organized and modelled data through the telescope of data science helps big and small businesses alike to understand their customers better. A plethora of usage patterns modelled into a working operative helps businesses take their next step for expansion, tapping into new demographics or revamping a product to suit customer needs.
R Programming for Data Science
R and Python are two high-level programming languages and are the most often used languages coded for data science and machine learning.
Data cleaning and transformation are important pre-requisites before attempting to build an operative model. R is the most widely employed programming language for this purpose.
It is a powerful language used for statistical analysis and data handling. It has many dynamic libraries which help in making data cleaning, transforming and visualization much faster. Building predictive data models is a particular strong suit in R.
Supra in Data Science
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