Data Scientists Myths VS Reality Explained by Walmart Labs Data Scientist
Can data scientists predict the future? Find out in this video where a Walmart Labs Data Scientist, Sumit Dutta, busts popular data scientists myths, and answers frequently asked questions. This video will give you a sneak peek into the life of a data scientist.
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Hi, my name is Sumit Dutta. I am a data scientist at Walmart labs for past one year. Before I proceed, I would want you guys to subscribe to Springboard India to stay updated for upcoming videos. I have been asked this a lot that data science is all about tools. So data science is as less about tools as computer is to computer science. There is a lot of science and a lot of work which goes before and after you actually come to using the tools. There is lot of exploration of data, a lot of evaluation metrics which you need to know and also background of what you are actually using, it’s an essential element when you come to data science. So I would say data science is far away from tools as you can imagine.
Another thing is that people often think that the coding background is a must to become a data scientist, so a lot of my colleagues and very smart people which I have worked with are not actually from computer science background, some of them are from electrical, some of them are from industrial. So it doesn’t really matter if you are from a computer science background or you have coding background because this is something you can pick up while you are working on data science and this wouldn’t really be as much of a blocker if you are willing to learn and if you have the nack of solving problems I don’t think that this is actually any hindrance to your progress when you are looking to be a data scientist.
I have also been asked opposite things as well that, can we become data scientist without actually indulging into coding ever, again this is also not true because as I have been talking that you are responsible of taking your ideas and your solutions to production. You need to know how to write production ready code once you are done solving the problem. So yes, it’s neither here nor there.
Also lot of times when I tell people that I am a data scientist, lot of people confuse it me being a data analyst, so there is a thin line between both the spaces. Analytics has a lot to do with inputs which are going on the backend of the things, meanwhile data science as a problem solving, lot of times we sort of contribute to what is going in the front lines. A lot of times, a lot of decisions and lot of important calls are made based on the work we do. So there is, analytics as a space and data science as a space are pretty far apart I would say.
So a lot of time people often mistake data scientist for an oracle, thinking that we can predict future. So predictive modelling is a very small part of data science. Data science is a very vast universe where a lot of streams and a lot of areas of expertise you can do. There is deep learning, there is language processing. You can use data science to translate one language to another. You can use data science to actually figure out if two images are similar or what is happening in a video stream. People are building self-driving cars out of it now. So predictive modelling is not really all we do. It’s a very vast space and predictive modelling is just a part of it.
So also a lot of time people tend to overlook basic concepts like probability, statistics, linear algebra. They think that they can overlook this and sort of get a hold of data science which not really is true because if you have to have a good understanding and a deep knowledge of what data science model is doing, how it is doing, how can you make it better and how can you actually use it to solve the problem which are presented to you, the knowledge of these three concepts is very imp