If I qualify for something, is that I have no technical background. 

I am a bozo at math and barely remember high school math. 

I was actually in surgical training when I decided to follow my passion and become a data scientist. 

But everybody was telling me this is impossible. 

"You need at least a degree in computer science, you need advance math, you need this, that..."

---OMG, what a mess I found myself into. 

Nevertheless I managed to make my dream reality. 

Not only have I become a data scientist, but I also taught myself machine learning, worked for a year as a data scientist, created 2 artificial intelligence companies and have 2 AI patents on my name. 

So with average IQ and no talent in math, how did I do that?  

STEP 1: 

REVERSE ENGINEER WHAT THE MARKET NEEDS IN TERMS OF SKILLS 

I started looking into the job posts and found the patterns of what the market needs. 

So I chose the tools mentioned most often and I focused only on them. 

I cold called recruiters and I pretended I was interviewing for my college assignment and they were eager to "spill their guts" of what the actually look when hiring data scientists. 

And that's what they told me: 

1. "We want people to use Python because it is open source and easier to deploy and has all these ready made libraries for data science. We are ok with R but not when it comes to deployment" 

2. "We are ok with Tableau and other visualization libraries but we prefer to not pay the monthly fee to these vendors if the candidate knows how to use python libraries to create nice visualizations that we need" 

3. "We do not care about academic degrees, but we want proof that the candidate can do her job immediately and does not need extra training. We need them to be hands on and ready to perform. We are a bit cautious with heavy academic degrees unless we are hiring for research positions, very rarely" 

STEP 2:  

LEARN ONLY WHAT THE EMPLOYERS WANT  

So I focused on learning python. I used the "Learn Python the Hard Way" book. It was enough to reach an intermediate level. 

Then I learned pandas for data analysis. I used the python for data analysis book. I used it as reference book and did not do all the chapters only the practical ones. 

I started immediately learning visualizations with matplotlib. I didn't like the original results, they didn't look nice, but then I started collecting template code with visualizations that I liked and reused them since. 

Then I immediately started applying my new found skills to any dataset I could get my hands on. 

STEP 3:  

SHOW DON'T TELL   

To satisfy the question of the employers asking for evidence of practical experience with data science, I immediately started analyzing datasets and publishing them in a blog. 

If you want to take a look into my blog and have a laugh at it, take a look here in the WebBack Internet Archive Machine. It looks super simple but the employers focused a lot on it and were asking me questions about details of each project to get convinced that I did not copy paste the code, and I was actually able to do it myself. 

This blog was enough to convince my first employer that I can do it. 

STEP 4:  

PLAY THE JOB MARKET WITH YOUR STRENGHTS   

I didn't have a strong technical background in computer science, and I blew lots of interviews that were asking general coding questions. 

I noticed that larger companies had this as a pre-requisite whereas startups were mostly focused on evidence that I can do it and a general "can do" mentality. 

So I started focusing my applications to startups. I played my healthcare background. I showed them that I can do it with my blog projects portfolio. And I researched their company thoroughly and was super enthusiastic about their mission. 

That's how I got my first job as a data scientist. 

It wasn't easy. There was lots of learning. Lots of rejection also that I wasn't used to. 

But I managed to get a job without any technical background. 

And remember this was 2013. 

Now it is 2020'ies. Yesterday I have seen that even IBM is skipping the Bachelor requirement from their technical positions. 

So it is much much easier today. And the demand for these skills is endless. 

I love my job as a data scientist and wouldn't change it for anything in the world. 

If you want to learn more about the shortcuts I used to learn only the necessary skills fast and efficiently take a look by clicking here

Till then, 

Alexandros Louizos 

ManXmachina  


Alexandros Louizos, MD
Alexandros Louizos, MD

Alexandros Louizos, MD is a vascular surgeon that left his career in surgery in 2013 to join the artificial intelligence revolution. After working for 2 years as a data scientist he decided to leave the corporate career to start his own company in 2015. He is a 2x entrepreneur of AI-related companies, Galaxy.AI (VC funded with $2.9M), and his latest venture is bootstrapped. He has designed and executed artificial intelligence systems currently in production is Fortune 500 companies. What gives him happiness is helping other dreamers to learn data science.

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