Role of AI and Machine Learning in Mechanical Engineering

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Artificial intelligence,machine learning, and data analytics. In this article today, we bring you the best career and interview advice from the real from real-life data scientists.  You can find people in the forums, you can talk to them and see and have this kind of a community where you can go to; you can go to data science meetups and meet people who are also the same, following the same paths, struggling to learn, etc. Then the third thing is, you’re having people who have already gone through this. Have some mentors.

I think that really helps a lot. Talking to them will make a lot more sense to you; you would also know where you’re going wrong and you can also say that this is the path I want to learn. There is going to be a lot of clarity which you’re going to get. And the fourth thing that I am going to say is this – Do not get stuck in theory.

It has to be hands-on. Unless and until you run your first model, understand and run your first model, it’s ok even if it’s a BlackBox, just run it. Even if you don’t understand python, just run it. . Be more hands-on. Only then you’ll learn a lot more. So, have a support structure of friends, forums, etc, have a couple of mentors or a mentor who is going to help you out and the fourth thing, Be Hands-on, do more projects.

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So Prashant I would say that breaking into data science is just equivalent to breaking into software engineering for someone who does not have that kind of background. To split it down into atomic parts, I would say that you need to be passionate about that field, you need to get a stronghold of the basics, basic technical skills that you require for that field. Apart from that, you should probably choose an industry in which you have an inherent interest. For example, if you’re interested in Finance, you should look for roles in the financial industry as a data scientist.

And apart from that, you should have a knack for augmenting your knowledge regularly because it is an ever-evolving field so every day you have new research papers being published, the amazing research that is happening in the AI and the community. And there a new tools that you get to use for implementing your solutions. So you should have that sort of curiosity and that sort of drive-in you to learn something new each day and keep augmenting your knowledge. If you feel you identify with this kind of a skill set you’re on the right path to transitioning into data science.

The thing is right now the people still have confusion that anyone can be a data scientist or not. So I will say anyone can be a data scientist. Even I am mentoring one student, he has absolutely no background in maths and coding and he is doing fine, very good in the data science track. So, regarding how can a person start with data science or thing. So, the curriculum is one thing that you can find and that you can do online or on some good platform.

The thing is you have to find a good platform, and a good mentor to do that, to guide you like these are the topics, these are the things, this is the track, this should be given to you and if a person follows that religiously, he is doing with good intent and learning and is very much motivated towards the course, he can be a data scientist and even he can be a good data scientist. A couple of advice to future aspirants of data science. Those are like this- Work on your critical thinking aspect; try to think, think with your data; ask these two questions – why and so what?

At every juncture whenever you’re given something, try to find a sense in it, a reason to yourself why I am doing this? And if I am doing this, so what? If we do get a solution, who will use the solution? And can we improve the solution in some fashion? The solution that I have in mind, can be improved so that it is more useful to the end-users. The second is, master the course structure. You’ll learn data science as you do in the field. Every day something new is coming to the field. So you continue learning and there will be no end to it.

But there are certain basic structures, certain basic foundations, that you should have and every hiring manager will want to look for those basic things in you. That is your basic maths, your basic programming, and their part of your Springboard curriculum. Please master those things. And third I would say – Have Patience! Even if you don’t make in 1-2 interviews, keep giving interviews, and definitely, you’ll be there. One of the biggest learnings which I have seen in whatever short time I have spent in data science is that in the initial phase of my career.

I found myself in an environment where I really did not have a mentor to go to or a person who can help me grow in data science. So, what happens when you’re in such an environment is that you tend to stay in your comfort zone and you just keep on those things which are very comfortable to you, what you have seen. But given how data science works or how vast this field is, this is not a very good sort of way to spend your time in data science. So now what happens in such a scenario after 1 or 2 years of your work experience.

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