What is Data Science ?

So what essentially data science is, its actually the soul which is underlying when you are doing analytics, business analytics, machine learning , artificial intelligence, deep learning. So all of these things are going to basically use the throughput or the engine which is data science. So if we talk about using analytics in our day to day activities we would have to understand that data science in essence is very important, we cannot do it without that. So what essentially is data science, before going into that,  lets talk about analytics first. If you are looking at a camera for instance which has a tripod stand and its basically the camera which is standing above the tripod stand. This tripod stand is giving stability to the camera. In the same way as there are three legs, corners or pillars, for this camera , in data science too we have three pillars. Statistics would be one of the pillars, the second one is Business and third is IT. Stitching together these three components leads to analytics. If we take it a little more deeper, add a little more complication in this , that is where we are taking it to the next level called as machine learning, Where we want to train a machine to learn from the data and make it a little bit automated so as to apply this learning on business problems. That’s machine learning for beginners. Now if we go a little bit more deeper, by increasing the complication of the algorithms , by taking inspirations from natural learning like the human brain or the gene structure , that’s where we start moving a bit closer to deep learning.

Now there are other aspects as well , we keep on hearing terms like reinforcement learning, Convolutional , Recurrent, feed-forward , modular and transfer networks , and so on. These are all variations with respect to the architecture of deep learning models. When we start talking about analytics or data science with respect to business it is called as Business Analytics, when used for social cause its called as Social Analytics, When we use it with respect to a domain like HR , we call it as HR Analytics, similarly Sales Analytics. When use with respect to Industry we call it as Retail Analytics, Financial Analytics, Healthcare Analytics, and so on. If we use it with respect to business problems we may hear of terms like Fraud Analytics, Risk Analytics, Machine Analytics, So there is a whole lot of sequence of terms which are used in the market with respect to industry, problem or domain and we should not be confused with all of these. What we need to understand is that data science is the core of everything. Now after all these clarifications let come back to understand what is data science. Its the science which helps us to learn from the data. Now essentially there are two things, either we can use an existing science or we can create one. Its the question of Discovery Vs Invention. Having said that when we have discovered a data science algorithm which is already in existence and use it for solving our business problems , then we are applying data science for application. If we are inventing new algorithms which is essentially not known in the topic domains then we are creating data science, this part will initially not happen in the data science career of an individual. In the early stages of a data scientist, we would be using existing algorithms rather than creating new ones which others would start using. Along with this in data science applications managing Computational science, memory management , big data management, real time vs at rest data, knowledge of full stack development would be very handy.

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