Big data, as a term, was coined in the year 2005. Post the era when the internet was introduced to us, the general public had to take a moment to take in the magnitude of the internet. While they were hit by a barrage of information, the government was wondering how to track and store the same. And as they grappled with an existing problem, the public eventually started adding to the pile of existing information. Eventually, Google and yahoo came up with innovations that helped in the storage of these with MapReduce and Hadoop respectively. Now that the issue of storing all this information was put out of the way, it was now time for sorting.
With more and more information coming onto the internet everyday, it became harder to go through or find the exact sort of data that was required by an individual. One could find a lot of junk data -as it is being termed- online. This came down to the advent of data science.
Data science is the application of algorithms, machine learning and various such methods and options for bringing out patterns by going through a lot of data. If you’re wondering what sort of patterns could a bunch of numbers have, well the kind that makes everything easier. For example, let’s say one would like to predict the kind of weather for tomorrow or maybe teach a computer how to play chess, one could feed all of this data and find out the probabilities of similar weather or train a computer to react by producing a certain counter move or moves. It gives one the ability to predict with a very high accuracy or learn and adapt from a newfound instance. Number-crunching, as it is sometimes called, is turning to be a necessity in an era where everything is backed by solid data and numbers that add authenticity.
Data scientists are essential analysts when it comes to research and content curation and extraction to make all of this data reader worthy and visual worthy. They become the backbone of providing valuable information in the junkyard of data we have to filter through everyday. As data representation becomes the new trend and the new demand, it may turn out to be a driving force in multiple platforms. From newspapers to machine learning, it’s everywhere already. All it takes to become one is inquisition and the mental set to be ready to work for it. As far as degrees and formal education go, it’s all good to have a PhD but not an absolute requirement. So start your data science training today and benefit with your newfound research capabilities as it becomes a secondary task at the back of the head.
The demand for data scientists is increasing day by day. Data science is a new technology and though not enough material is available on the internet to study it. Reputable institutes are teaching data science to their students. But students from other institutes can also study data science course.
by Shalini M