Data Science and Artificial Intelligence Career Advice | Volume 4

Wish to build your career in Data Science and Artificial Intelligence space? Here is a video with the best advice from subject matter experts. Listen to these data scientists share tips on how to become a data scientist and how to crack a data science interview or an artificial intelligence interview along with advice on how to make a career transition into AI, Machine Learning and Data Science.

0:33 Data Science Career Advice by Youplus Data Scientist
1:29 Data Science Career Advice by Gramener Data Scientist
4:06 Artificial Intelligence Career Advice by Swiggy VP-Head of Applied Reaserach

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Hey everyone, many of you have reached out to us to seek advice on a career transition into the field of data science, artificial intelligence, machine learning, and data analytics. In this video today we bring you the best career and interview advice from the real-life data scientists but before we get started do not forget to subscribe to the Springboard India channel to stay updated on the upcoming interviews with real-life data practitioners.

Hi, my name is Shweta, and I am a data scientist at Youplus. I build products using natural language processing and deep learning. A lot of data science aspirants have asked me this question that how can they successfully transition into a data science job if they have started as a data analyst or as a software engineer and once they have decided to pursue data science, what I suggest would be you know you should identify where your strengths are, whether it is machine learning, whether it is inferential statistics or exploratory data analysis that you are interested in and the hone those skills and I think mentor, a good mentor can help the learner identify strengths and in the long run how this helps the learner is that they can build a lifelong relationship with the mentor and they can be available for them for making future career decisions as well.

Hi, my name is Jaidev Deshpande. I work as a senior data scientist at Gramener where we build tools and applications that help people narrate stories with data with machine learning and we help people extract insights out of their data. So there is nobody who doesn’t use data science. It’s ubiquitous, it's omnipresent, it’s everywhere and also equally importantly there is no specific skills as such that are inherent in people that make them a good data scientist. So whatever is required to be a good data scientist can be found in anybody and in fact, I don’t think we should even call them skills. Most of the things or the prerequisites that are required for being a good data scientist or for practicing data science, they are more like habits, they are not skills. So the more you practice programming, the more you practice mathematics, the more you practice statistics the better you are at it and all of these things can be learned. There is nothing that can not be taught, there is nothing that can not be learned. I don’t think there is anything inherent about a data scientist that distinguishes them from the remainder of the people. In fact so much so that I believe that people are learning data science at a younger age, soon this is going to become a fairly common skill in a lot of people. So, ya we are definitely on the right path over there. As far as mentorship is concerned I believe that when you are learning data science when you begin to learn data science, you tend to get a little lonely. There are very few people in your friend circle, family, around you in general who are working on the same problems as you are, who are interested in the same problems as you are. So simply having somebody to talk to is more than half the battle won, that helps a lot. And secondly, you can always learn from the mentors’ experience.