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. You will also find tips on how to build an effective resume. 0:33 Data Science Career Advice by Walmart Labs Senior Data Scientist
1:36 Data Science Career Advice by Walmart Labs Data Scientist
3:09 Machine Learning Career Advice by AiFonic Labs Machine Learning Scientist
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To give you some advice, so we interview lot of people and what I find that people are just coming into data science because they have heard so much about data science. I wanted a person who wants to go in data science field, he should be interested in it. He just wants to you know explore that field. He should love it. He should not move into data science field because of the salary or what you have heard around it. The way I, when I started my career I never chase my company or job just because of salary, mostly because of the projects that I want to do it and I want to enjoy my work. What I have seen in some of the interviews that people come and they just try to you know fake things, that okay I have done that and I have done that..its always good when you come in to the interview for a data science thing, if you don’t know things, you can directly tell because we are not looking for a perfect person, we are looking for a person who can learn and who has really interest in data science field. So finally my final advice is just go into data science because you love data science. Just don’t go in data science because you want more salary in data science. 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 amongst others is very important. These are the fundamental building blocks which you would require, the basic things which you would require when you transitioning into data science as well.
Talking about transitioning into data science, what I would want to say to people who are looking to transition is that first of all you should have a very good clarity of why you want to transition into data science, what excites you about data science. So that you can grasp whatever concepts are there with equal enthusiasm and also never to overlook the basics which are been talked about. Lot of people just see very cool problems today which are being presented out there and try to straight away jump to solving that like image or deep learning but what I would suggest to them is to start up from the basics. Do not skip over that because if you do it correctly you will actually get a better hang of what you are trying to do and you will be a much more versatile and a much more well equipped data scientist. So talking about transitioning to data science, let me also tell you my own story. So while I did masters in statistics and computer graphics, I was a developer before I became a machine learning scientist. So I was a developer but I wanted to transition and at that time like over 10 years back when I tried to transition, there weren’t a lot of resources like there are today. There wasn’t even coursera for instance and the path that I chose to transition.
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