Big data, as a term, was coined in the year 2005. After the era in which the Internet was introduced to us, the general public had to take a moment to absorb the magnitude of the Internet. As they were hit by a barrage of information, the government wondered how to track and store it. And while dealing with an existing problem, the public finally started adding information to the existing stack. Eventually Google and Yahoo came up with innovations that helped in storing these with MapReduce and Hadoop respectively. Now that the problem of storing all this information was eliminated, now it was time to sort.
With more and more information pouring onto the internet every day, it became more difficult to review or find the exact type of data an individual required. One could find a lot of junk data, as it is called, online. This came down to the advent of data science.
Data science is the application of algorithms, machine learning, and various similar methods and options to generate patterns when analyzing large amounts of data. If you’re wondering what kind of patterns a bunch of numbers could have, well, the kind that makes everything easier. For example, let’s say one would like to predict the type of weather for tomorrow or maybe teach a computer to play chess, one could feed all this data and find out the probabilities of similar weather or train a computer to react producing a certain counter movement or movements. It gives one the ability to predict with very high accuracy or learn and adapt from a newly discovered instance. Number crunching, as it is sometimes called, is becoming a necessity in an age 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 this data readable and visually worthy. They become the backbone for providing valuable insights into the data junkyard that we have to sift through every day. As data rendering becomes the new trend and new demand, it may become a driving force across multiple platforms. From newspapers to machine learning, it’s already everywhere. All it takes to become one is the inquisition and the mindset to be ready to work for it. In terms of degrees and formal education, having a Ph.D. is nice, but not an absolute requirement. So start your data science training today and benefit from your new research capabilities as it becomes a secondary task in the back of your head.
The demand for data scientists is increasing day by day. Data science is a new technology and although there is not enough material available on the internet to study it. Renowned institutes are teaching data science to their students. But students from other institutes can also study a data science course.
