THEORETICAL ASPECTS OF DATA
Data is everywhere. It is probably floating in millions of bits and bytes around you as you are reading this. It forms the very basis of the existence of not just human life, but of life in totality. From the very DNA in your cells to the very particulate miniatures of soil around you, anything and everything you see around you has a bit of a story to tell. And how exactly is this story represented and brought forward to the world- through its data. There exist an infinite list of parameters and categories in which a data set can be classified and split in order to get a better understanding of it.
DATA IN THE WORLD OF COMPUTATIONAL STUDY
We saw how each and everything in the universe packs some amount of history with it which is subsequently displayed and interpreted in the form of its data. This very statement should more than suffice to highlight the importance of data in the real world. However, the importance of data and its interpretations grow a thousand-fold when we consider the world of computer science and its applications (such as IoT, AI, designing, among the few names in a seemingly never-ending list). The reason for this importance is fairly simple. In a world where every question asked in the world can be answered and traced back from its point of origin, there is nothing better than all of its corresponding data that can tell us better about its entirety. This data when thrown towards the smart and highly powerful computers of today can reveal things and information that pack with them the ability and potential to bring about really sophisticated changes in an institute, or an enterprise, or an organization, and in some cases even in the world. It is this very concept and procedure of deriving things and information of value to a given field, which gave birth to the concept that we are very familiar with today as data analytics. The concept of data analytics saw itself incepted way back in the early 2000s, however, it started to gain momentum and importance somewhere around 2012. And just 6-7 years later, has become one of the most leading, prominent, and promising fields of computer science application in each and every field of the modern world.
DEFINING DATA ANALYTICS FORMALLY
The layman definition of data analytics is pretty straightforward- “Data analytics is all about finding commonalities and common points of talking and reference in a large data set that can be made use of to derive and come up with results and findings that can be put to professional use in order to help a community to tackle and solve a real-life problem for them.”This definition, however, seems too long and cumbersome, which is all okay because it is a casual and layman definition. A more formal definition of the same could be put in the following words- “Data analytics is the qualitative and the quantitative approaches used to identify behavioral data and patterns in order to enhance productivity and gains of a particular community.”
The traction gained by the field of data analytics is something that cannot be expressed in mere words.The biggest point of consideration with it is that its value and demand is only set to grow with the passage of time. As such, choosing a professional #datascience#dataanalytics
Article author : rakshit b v
Article source name : data science
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