Sorry, You Can’t Stand Out in Your First Year of Learning Data Science

Do you feel like, these days, every other student wants to break into data science?

Does it look like everybody on LinkedIn is doing the same Andrew Ng’s machine learning course?

Do you run out of hope that you’d be recruited as a data scientist amongst the sea of data science enthusiasts?

If you nodded your head, don’t worry. You’re not alone. My mentees, at some point, ask me the same question: “if everyone else follows your path and succeeds, then how do I stand out from the crowd?”

Look, I don’t gain anything by sugar-coating a lie and selling it to you. You might clap for this article, but I can’t live with that. I want to be honest even it becomes harsh while giving you the right mindset.

You still there? Good. Don’t tell me I didn’t warn you.

This article aims to detail standing out in data science, but the short answer is: You can’t stand out early, and nobody expects you to. If you stay in the game long enough, you will win because everybody else will quit.

Let me explain.

You Are Not Expected to Stand Out Early

In 2017, when I started to learn data science, nobody knew me.

I was in my hostel room navigating through a bunch of courses on Coursera and YouTube videos. I would binge-watch Andrew Ng and every YouTuber who talks about the skills needed to become a machine learning engineer.

It was my learning phase. The last thing on my mind was to stand out.

I preach a 3 step process to break into data science. Learn, Create and Share. It was only when I started creating and sharing data science content — people started to notice me. I kept going and got multiple job offers, freelance opportunities and a loyal audience to read what I write.

Now I don’t intend to brag about all these. I’m just transparent to show you the process and the results. When you start learning, nobody will notice you. Nobody cares if you’re doing Andrew Ng or a nanodegree from Udacity. And that’s OK.

No recruiters are looking for data science learners who are standing out. They want the learners to learn all the basics have experience working on real-world projects. So let me rephrase the answer to your concern. Even if you stand out as a data science learner — nobody is looking for you.

And that’s OK. Please trust me on this.

It Applies to Anything in Life. Think About It.

This principle isn’t specific to data science. I have experienced it in all phases of my life.

When I started writing on Medium, I didn’t have an audience. I was just practising in public and improved my craft as I wrote more. When I posted on LinkedIn, nobody noticed initially except for my dear friends. These days I’m starting freelance writing — I don’t have many clients. It’s normal.

I can’t think of any chapter in my life where I stood out instantly. Can you? I thought so. Then why would you expect to stand out in data science so early?

Nicolas Cole wrote a beautiful piece that outlined how the first year was the hardest, citing various aspects of his school, work and personal life. I can’t agree more.

Your first year of learning data science is going to be the hardest. I have met countless enthusiasts who quit data science within 6 months and went back to software engineering.

I’m okay with that.

Not everyone has to become a data scientist.

But if you really want it, keep learning and growing. There’s very less competition at the top. Please trust me on this too.

How to Stand Out as a Beginner Data Scientist Eventually

First, let’s summarize what we have understood so far.

• Nobody expects you to stand out when you’re learning data science.
• Only when you create and share in data science, you’ll be noticed.
• You keep learning all the basics and improve your skills day by day.
• Many enthusiasts quit within a year, leaving you very little competition in the long run.

If you’ve understood the above and mastered all the basic skills — now you’re in a good position to stand out. I could have told this initially, but then you’d have rushed to implement it even before mastering all the basics.

The only way to stand out is to do things other people are not willing to do. It’s a simple idea but also the hardest to implement. It consumes a lot of time but still pays out forever.

One of my first projects at work involved scraping restaurant-related data from multiple websites like Yelp and TripAdvisor. I cleaned all that messy data, built a web application with useful visualizations and presented it to my CEO. I don’t need to tell you if the CEO was impressed — you’re smart.

One of my friends went to the supermarket, recorded clips of the shelf with the grocery items. Labelled each image manually and built a system that automatically alerts when an item is about to go out of stock. He made the system live when he was applying for jobs. I don’t need to tell you how many offers he was bagging — you’re smart.

I can go on and on with multiple examples, but you’re a smart person. You got the idea. Put in the effort and time to do something others are unwilling to do (only after you build your foundational skills.) Here are some ideas on what you could do:

• Don’t stop at building machine learning models. Develop a machine learning web app.
• Don’t stop after building a web app; wrap it in a docker container. Show them you know MLOps too.
• Don’t stop at MLOps — deploy it in the Cloud. Show them you’re comfortable with the Cloud.
• Don’t always use the data from Kaggle and UCI — give web scraping a try (affiliate link). Show them you’re willing to go beyond and above.
• Don’t stop at being a good data scientist. Share all your knowledge through content. Be a great data scientist.

Finally, I can’t teach you everything; part of standing out is to come up with your own ideas. I wish you all the very best.

For more helpful insights on breaking into data science, honest experiences, and learnings, consider joining my private list of email friends.