The World-Class Data Scientist
By: Mohammad Ahmad
Tags: Machine Learning, Data Science, Technology, Education, Programming

We’ve all seen the ‘controversial’ Medium articles that say that MOOC’s don’t make you a Data Scientist, and also the ones that say you should get Certificates accredited by various organizations to prove that you’re actually worthy of calling yourself a Data Scientist.

Whilst it may be true that these various pathways alter the chances of success, are they really sufficient in being able to call yourself a successful Data Scientist?

To call yourself a success in any field is a subjective matter so it’s important to first know what you want out of a journey. Do you want Money? Freedom? Do you want to just have a casual understanding or do you want to go toe-to-toe with the world's best academics?

To be a success , or even world class is something that I think is hugely subject and changes over time, but from my experience of working under and with some of the best minds in the world: the following are simply perquisites:

Know what you don’t know

What’s your goal and where do you see yourself 10 years from now? When I started on my journey, I was 19 and had a vague picture of where I wanted to go and like many of my contemporaries, serendipity played a big hand on the path I took.

A good teacher and a good guide is a fantastic starting point. Speak to your role model and if you don’t have one: find one. Then you need to make a list of everything you need to know and cross-check your current knowledge set against where you stand.

Photo by Daniel Mingook Kim on Unsplash

Learn about who the experts are in what they’re doing

Keep an eye on the top 3 or 4 people or institutions in the field. Regardless of which discipline you’re in, it’s always important to know where the bar is and where you stand on the totem pole.

Benchmarking is central to the theory of Data Science and likewise, it should be central to your perspective in your discipline.

We’re in an era of perfect information and in an era of openness, use that to your advantage. Watch their youtube videos and read their academic papers, and most importantly, try to get in touch and have a chat!

A few times in my career, I overlooked this point. Missed opportunities or paths I should have trodden further down are now just memories, but, we learn something from these mistakes. Get good at spotting what you like and don’t like.

Make your foundation strong

When I say learn, I mean you really have to learn. Data Science and Machine Learning require top of the line Mathematics and Statistics. I’d recommend not using sklearn or other out of the box tools (yes, that includes Tensorflow ) until you can stand for yourself and take on the big guys in advanced discussions.

I’m really sorry but this is debatably the most important part of the whole story. You need to work your ass off and make sure your maths and stats are up to scratch. You want to watch youtube videos, download pdfs, or even learn whilst making a project — but please, know your weakness and keep fighting it!

Seriously, the best data scientists aren’t geniuses: they just work harder than everyone else.

Make projects with others — get feedback

This is really underrated. Working with others on Data Science or Machine Learning projects gives you a deeper understanding of the problem you face because your partner will generally have a different perspective.

Everyone has their own story and background so it helps to really diversify your view. Mathematical techniques are gold-dust in Data Science and whether you’re confused on why you should choose L1 over L2 regularisation on a simple regression problem, or debating on how many layers to have in your DNN , these kinds of questions only come to you when you have to justify your actions with other people.

Photo by Dmitry Ratushny on Unsplash

Build something that fixes a problem

Think about your own life and think of something that makes you more than 10 minutes to do. Turn this into a list and go through them one by one. Can you use Machine Learning or Data Science to tackle them? I assure you — you can.

Fixing problems isn’t easy and a lot of the time searching for problems often gets you into a convoluted mess. However, understanding the lifecycle of problems get you familiar with the whole ecosystem that we live in.

We’re in a time where our lives feel symbiotic: half of it is online and the other half is idly walking around the screen to screen. There are so many problems around us: often, you just need to look with a different lens for them all to be clear.

Go back to square 1

So you learned a bit, you taught yourself a bit, you solved a problem and now you’ve accomplished your mission. Now what? Well, this comes back to my first point: know your end goal.

If you’re still not satisfied, re-group and speak to an expert again or reach out to who you look up to. It’s not easy, but this perpetual loop of taking a look at how your career is shaping is a key way to ensure that you are on the right path.

I assure you if you keep repeating this loop, you’ll make it to the top. If you don’t believe me, comment below and I can give 101 examples of friends who’ve succeeded far beyond belief by following the top pattern.

At times the path is steeper than others, so make sure to stay focused and stay cognizant of your current position. You can never be too sure when your next break will be.