I know how it’s like discovering data science and falling in love. The field is exciting and enticing, and you’re determined to make it a data scientist even when you don’t have the skills.
Sadly, with all the hype also comes misinformation. Someone says to do a certification, and another deems it useless. Recruiters make it worse by asking for all the skills in the world, making it harder for beginners even to fathom how they can gain experience if nobody is ready to give them a chance. Some go into debt to pursue Masters in the United States, hoping to repay once they start working.
The myths overwhelm and derail you from the journey, leaving you frustrated, stuck, and broke.
I know it, my friend, because I went through the same a few years back. I understand how you feel, and that’s the sole reason I write these articles, hoping to help at least some of you.
However, remember this: there are many paths to break into data science, and I’m only showing you one. I’ve written a roadmap to break into data science, my learning and experiences working in an AI startup, things to know if you want to switch careers, and my biggest mistakes early in my career.
This article will see common myths and misconceptions that are flooding data enthusiasts. You’ve likely heard them too, and it’s time we bust them.
1. You Don’t Need to Come From a DS/CS Background to Do Well in Data Science
This is one of the most common concerns amongst data science enthusiasts when they come from a different background or want to switch from a different domain.
You think they’ll always prefer someone from a DS/CS background. You assume there’ll be an unconscious bias against you. You feel you might have to start from scratch, even if you have multiple years of experience in a different industry.
Look, I understand all your concerns — if someone with 12+ years of experience in retail felt that way, if someone with a Ph.D. felt that way, your fears are natural. But this is a myth.
I can’t talk for all companies, but I’ve connected with countless data scientists who are coming from physics, economics, electronics, mathematics, statistics, mechatronics, journalism, marketing, and other domains. I’ve colleagues with various backgrounds equally competent as me who come from a CS background.
Guess what we all have in common?
What you need instead:
Data science skills. It’s that simple.
You can’t pass through any interviews without the relevant data science skills. Interviewers don’t care about your background; they’re more interested in knowing your skills. Nobody will reject you when you have the skills and can demonstrate them (more on this later.)
Here are some of my recommendations for you to get started:
- A learning roadmap: Your Data Science Journey Kickstarts Here
- The 4 Must-Learn Data Science Courses for Absolute Beginners
- The 3 Must-Read Data Science Books for Absolute Beginners
I’ve conducted hiring interviews, and though I’d like to know about your background, I focus solely on your ability to solve data science problems. If at all, your non-DS/CS background is attractive to me because it shows me that you’ve put in an extra effort to acquire skills.
If at all, I want you in my team — get it?
2. You Don’t Need a Master’s Degree to Become a Data Scientist
Let me be honest here — I believed this myth for a long, long time.
I used to prepare for the “MS in US” dream with GRE, TOEFL, and whatnot. I feared the idea of going into debt to pay for a degree that I couldn’t afford, Thankfully that stopped me, and much later, my mentor (who also turns out to be a Google Engineer) busted the myth.
Here’s what he said:
I got my first data science job as a fresher without prior work experience. I didn’t believe I’d be able to bag it — until I did. The interview questions were tricky. However, I had just finished Andrew Ng’s courses, so all concepts were fresh in my mind.
Then they gave me a take-home assignment and asked me to present my solution to the entire team a few days later. The presentation I delivered got me the job. I spoke about how I had solved a similar problem before and what challenges I had faced.
You can have a Master’s and still get rejected, and not have a Master’s and still get hired — get it?
What you need instead:
You need to have a portfolio of projects that demonstrate your skills. Naturally, they will be curious about your work, and you’ll end up talking about this for about 50% of the interviews.
There are many ways to go about this. Here’s what I did:
- Build projects publicly on GitHub. Aim for a simple project, and improve it on the go.
- Create a simple portfolio website using GitHub pages for free.
- Add all relevant links to your CV.
Doing this would tremendously improve your chances of getting hired. You don’t need a Master’s; you need a portfolio.
3. You Don’t Need 100% Of the Skills Listed on the Job Description to Get an Interview
Sebastián Ramírez needed 4 years of experience in FastAPI, which he created 1.5 years earlier. Moez Ali, the creator of the PyCaret library, had to have 3+ years of experience in PyCaret, a library he created only a year earlier.
If it wasn’t for the authors of the book “Build a Career in Data Science,” I might not have courageously applied for most of the jobs.
Here’s the thing: Most job descriptions are wishlists of hiring managers who don’t want to miss out on the ideal candidate. They know the ideal ones hardly exist and are perfectly fine with someone who doesn’t 100% match the requirements they put out.
The rule of thumb is to apply whenever you meet 60% of the job requirements as advertised by the recruiters. But there is a better way.
What you need instead:
You need to share your work publicly so that recruiters can see and reach out to you.
I do this now religiously. If I’m creating something, I do it publicly. I share the challenges I faced and the learning on LinkedIn.
Why LinkedIn? That’s the platform every recruiter is guaranteed to search for talent.
The result? On average, I get 3–4 recruiters reaching out with potential interviews.
I feel sad when I hear from beginners that they’re unable to get to the interview stage, leave alone getting hired. I’m not here to brag, but tell you what I did, and the results reaped.
I know why you read till here. Myth-Busting is important, especially when there’s a lot of uncertainty in your mind.
But guess what’s more important? The ability to translate this knowledge into action.
It’s the part where I urge you to take action — because that’s what matters.
- Acquire relevant data science skills online. With all the courses I’ve recommended, and most of them free, which you can learn from home, you have no excuses at all. What background you come from doesn’t matter at all.
- Create a portfolio of projects that demonstrate your skills. Use this guide to get started. This is what you’ll end up talking about in 50% of your interviews.
- Share your work publicly. It opens up the world to make connections, get genuine referrals and even force recruiters to reach out to you. Isn’t this how you found me anyway?
Remember, nobody said this is going to be easy. If it was, it’s not a big deal to break into data science. Getting your first job in data science is the hardest. It can only get better from there. How about using that as your motivation?
For more helpful insights on breaking into data science, honest experiences, and learnings, consider joining my private list of email friends.