Data Scientists Myths vs Reality Explained by Datakalp Founder and Chief Data Scientist

Is math a must have to get a data science job? Are data scientists geeks? Find out in this video where Datakalp Founder, CEO and Chief Data Scientist, Dr Kalpit Desai, busts top 5 data scientists myths, and answers frequently asked questions. This video will give you a sneak peek into the life of a data scientist along with a career advice for your successful career transition. Here is a video explaining the most common mistakes to avoid in your data science career transition http://bit.ly/2TBPAbB

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let us also talk about what are the frequent rumours and myths .which you might have also have heard from your friends from other people from other professionals so the first myth that I would like to talk about is data science is all about tools and this si certainly a myth tools because tools is just one piece of your journey of your skill sets tools also come and go tools evolve with time better tools come in better processes also comes in what is more important is how do you know what do you do with these tools and I will take an analogy here lets say you are a carpenter and you know how to use hammer how to use saw and so on just the knowledge of how to use those tools doesn’t enable you to construct a house you still need to know how do you use those tools how do you put all these pieces together which tool you apply where and that is what enables you to build the house data science is similar to that just knowing the tools is not enough you also need to know where to apply which tool you don’t apply certainly needle where you need hammer you don’t apply sword where you need a needle so each tool has different application and you really need to know how to put all of that together and that’s what matters.
So here is the very funny myth, are data scientists geeks? Depending on how you define geeks, certainly data scientists need not be a geek its not that you have to be a nerd, antisocial, social person to be a data scientist. Some aspects of data science like focus deep thinking and so on do help if one has introvert kind of nature, but that’s absolutely not necessary to be an introvert or to be a geek to be a nerd to become a successful data scientist.
Often you hear that data science is same as data analysts, data scientists are essentially data analyst. Now there is no standard definition of these terms and that’s where these terms are getting over used hype doesn’t help here always so but in industry more or less you will see that data science is more about building algorithms working with data also writing the fair amount of code you will be writing as data scientists whereas data analyst getting insights form data they can feed into a business decision they can feed into a report for example. So there is fair bit of difference between data scientist and data analyst. Data analyst is for example not required to code most of the time but it is he/she is required to work a lot with data. Data scientist may spend 50% of their time coding and lesser time worrying about how this data is feed into the business decision so yea there is a difference.
Data Scientists can predict future? Of course there is no data scientist that can actually predict future what is true in reality is based on the historical data and when that data is sufficiently comprehensive sufficient volume is there for the data sufficient time duration is covered for the data it has been shown that you can for example predict results of elections results of some games and so on but again that is all based on the patterns which are mined.