Data Science & Artificial Intelligence Career Advice by Real-Life Data Scientists

Data Science & Artificial Intelligence Career Advice by Real-Life Data Scientists
Data Science & Artificial Intelligence Career Advice by Real-Life Data Scientists

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 Post today, we bring you the best career and interview advice from the real from the real-life data scientists.

If you want to start AI tomorrow there are three things I would say that you should have. One is figure out what kind of learning do you like? Do you like to learn from a book or do you like Our Post.

Those kinda things, figure out your learning curve, how are you going to do that. The second thing is, find more friends. You can find people in the forums, you can talk to them and see and have this kind of a community where in you can go to; you cango to data science meetups and meet people who are also the same, following the samepaths, struggling to learn, etc.

Data Science & Artificial Intelligence Career Advice

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Then the third thing is, you’re having peoplewho have already gone through this. Have some mentors. I think that really helps a lot. Talking to them will make a lot more senseto you; you would also know where you’re going wrong and you can also say that thisis the path I want to learn.

There is going to be a lot of clarity whichyou’

e going to get. And the fourth thing that I am going to sayis 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, justrun it.

Download a notebook and just run it on a GoogleCollab or whatever it is, but just run it. It’s OK. Be more hands-on. Only then you’ll learn a lot more. So, 3 things: Figure out the course, whateveryou want to do; have a support structure of friends, forums, etc, have a couple of mentorsor a mentor who is going to help you out and the fourth thing, Be Hands-on, do more projects.

So Prashant I would say that breaking into data science is just equivalent to breaking into software engineering for someone whodoes not have that kind of background.

To split it down into atomic parts, I wouldsay that you need to be passionate about that field, you need to get a stronghold of thebasics, basic technical skills that you require for that field.

Apart from that you should probably choosean 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 knackof augmenting your knowledge regularly because it is an ever-evolving field so every dayyou have new research papers being published, the amazing research that is happening inthe AI and the community.

And there a new tools that you get to usefor implementing your solutions. So you should have that sort of curiosityand 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 ofa skill set you’re on the right path of transitioning into data science. The thing is right now the people still haveconfusion 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 absolutelyno background of maths and coding and he is doing fine, very good in the data sciencetrack. So, regarding how can a person start withdata science or thing. So, the curriculum is one thing that you canfind 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 ofdata science. Those are like this- Work on your criticalthinking aspect; try to think, think with your data; ask these two questions – whyand so what? At every juncture whenever you’re givensomething, try to find a sense in it, 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 thesolution? And can we improve the solution in some fashion? The solution that I have in mind, can it beimproved so that it is more useful to the end-users.

Second is, master the course structure. You’ll learn data science as you do in thefield. Everyday something new is coming to the field. So you continue learning and there will beno end to it. But there are certain basic structures, certainbasic foundations, that you should have and every hiring manager will want to look forthose basic things in you.

That is your basic maths, your basic programming and there 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 haveseen in whatever short time I have spent in data science is that in the initial phaseof my career I found myself in an environment where I really did not have a mentor to goto 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 isafter 1 or 2 years of your work experience when you look back and see what all have youlearned, there is not a lot on your plate. That’s something that I would suggest peopleavoid, especially early on, in the early stages of their career.

Tell your story! What I mean by that is, if you have a portfolio,if you have had a bunch of things that you’ve done especially in data science, the journeymatters a lot more than the outcome.

So, like I said, we need to see your approach,so there was this problem, where did it come from? Why did you have to solve it? What did you do in order to finally solveit? If you failed what could you’ve done differentlyif you had the benefit of going back in time? So it all, I guess it’s encapsulated intothat one statement when I say that “Tell your story properly and faithfully”.

Write only things that matter, things thatyou’re clear about. Write only about your own contributions. And be clear about it you know like be clearabout your expectations, and in general, make it a short CV. Don’t make it 30 pages long.

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