929 5555 929 Online | 929 5555 929 Classroom hadoopwebmasters@gmail.com

Understanding the concept of data science in-depth

 

Data science isn’t something that was invented or discovered recently. Companies like Google and Amazon have been using this for many years now. For example, what Amazon did in the 90s where they created amazon.com and built recommendation engine is also data science though it wasn’t called so back then. So, data science wasn’t something new that came out of the blue; it is just there, but the name data science has been coined recently. So, essentially data science is all about extracting meaningful and actionable information from data.

 

As long as data was small or at least manageable dealing with it was easy. But now with the advent of Big Data, in terms of volume, velocity and variety, the challenge is to create Big Data solution that can facilitate data science. If there are lots of data we need systems to store, manage and analyze it. For example, let’s take Twitter; the data science challenge there is to analyze data in a very fast paced manner. Similarly if we have the variety of data the data science challenge is how to extract meaningful information from these diverse formats of the data and then make some inferences.

 

Data science isn’t an absolute term; rather it has flavors of multiple components including statistics, data engineering, visualization and advanced computing along with domain expertise. Depending on the work platform, the work of a data scientist can be different. For example, the role of a data scientist in the field of statistics is completely different compared to say the role of a data scientist in data engineering, which is more like the backend processing.

 

To understand this more deeply, an expert once said that the data scientist’s role is like that of the webmaster of the 90s when the internet just started coming. Back then, people were new to web services and yet every company was trying to design some kind of a web service that was useful for their customers. But since most people didn’t have a clue of how to build one, the role of a webmaster or web administrator was created, where this web administrator was supposed to solve all the problems related to designing web services whether it is frontend or backend or communication between the two. Hence, a web administrator was supposed to understand all of that. Hence, a webmaster was the magic pill responsible for solving all the problems related to designing web services.

 

Obviously, that isn’t the right way to operate and over the time as people got to understand web services more, different roles were carved out within this space, like frontend engineers who design the UIs, network engineers who design the network backend for communication between frontend and backend and also database specialist, depending on the backend being used, there are specific people who manage the backend. In a similar manner, in the next 20 to 30 years, data science teams rather than just data scientists, with different skill sets might come together to take the place of today’s data scientist.