9 Reasons Why You’ll Never Become a Data Scientist

Disclaimer: This story is not meant to discourage you. Rather, it should serve as a long hard look in the mirror.

So you’re passionate about data science, you’ve read a few dozen blog posts and taken a few online courses. Now you dream of making it your job. After all, it’s the sexiest job of the 21st century, according to Harvard Business Review.

But despite your enthusiasm, Data Science may not be for you. Right now you have too many illusions and false stereotypes.

Greetings, humanoids

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Now your task is simple: remove the things that are holding you back! And you will be surprised how fast you go.

1. You think your degree is enough

You have a master’s degree in a quantitative field, or maybe even a Ph.D. Now you want a head start in data science.

But have you ever used a shell before? Have you felt the intimidation that can come from command line interfaces when you run into errors? Have you ever worked with large databases — terabyte scale?

If you answer no to any of these questions, you are not ready yet. You need real-world experience and building real projects. Only then will you encounter the kind of problems that you will face daily as a Data Scientist. And only then will you develop the skills to solve them.

Congratulations on your graduation. Now get into the hard work.

2. You lack passion

Have you ever invested an entire weekend in a geek project? Have you ever spent your nights browsing GitHub while your friends were partying? Have you ever said no to your favorite hobby because you’d rather be coding?

If you couldn’t answer any of the above questions with yes, you’re not passionate enough. Data science is about facing really tough problems and sticking with them until you find a solution. If you are not passionate enough, you will recoil at the sight of the first difficulty.

Think about what attracts you to becoming a Data Scientist. Is this the glamorous job title? Or is it the prospect of sifting through tons of data on the search for information? If it’s the latter, you’re headed in the right direction.

3. You’re not crazy enough

Only crazy ideas are good ideas. And as a Data Scientist, you will need a lot of them. Not only will you have to be open to unexpected results, they happen often!

But you will also have to develop solutions to really difficult problems. It requires a level of extraordinary that you cannot achieve with normal ideas. If people are constantly telling you that you’re off your rocker, you’re headed in the right direction. Otherwise, you will have to work on your craziness.

This of course requires a certain audacity. Once you blurt out your eccentricity, some people will scratch their heads and turn their backs on you. But it’s worth it. Because you are true to yourself. And you ignite the spark of awesomeness you need as a Data Scientist.

4. You learn from textbooks and online courses

Do not mistake yourself. Textbooks and online courses are a great way to start. But only to start!
You need to work on real projects as soon as possible. Of course, there’s no point in building a Python project without being able to code a single line in Python. But once you’ve built a modest foundation, get active.

Learning by doing is the key. Start creating your GitHub portfolio. Take part in Hackathons and Kaggle competitions. And blog about your experiences.

Anyone can make textbooks. To be a Data Scientist, you have to do more.

5. You think you can stop learning at some point

You’ve subscribed to a few online data science courses and are reading a few textbooks. Now you think that once you’ve mastered them, you’ve learned enough to break into data science.
Bad. It’s still the beginning. If you think you’re learning a lot now, think about what you’ll learn three years from now.

If you become a Data Scientist, you will learn ten times more than you do now. It is an ever-evolving field where new technologies are constantly needed. If you stop learning once you’ve landed your job, your trajectory is going to go from a data science newbie to a data scientist who sucks.

If you want to excel in data science (and if you’re reading this, you do), you have to face the fact that your learning curve will get steeper over time. If you don’t like learning Bigly, stop dreaming of being a Data Scientist.

6. You don’t have expertise in another area

So you know a thing or two about computers, and your math skills aren’t that bad. Will you be able to land a job in data science?

No, you won’t. Your computer and math skills are essential, but not enough to set you apart from all the other Data Science enthusiasts. Data Scientists work in all kinds of companies and all kinds of industries. To provide key information to your customers, you need knowledge about their domain.

For example, Kate Marie Lewis from the story below landed a job in data science within six months. But what made the difference was that as a neuroscientist, she had knowledge in the field of health.

What area are you good at? What areas do you have experience in? Try to position yourself as a specialist in your field, and less as a generalist Data Scientist. This is how you really land a job.

7. You lack business skills

So you are more of an analytical type. You like numbers and quantitative analysis, and you hate soft skills and human interactions.

That doesn’t make you a good Data Scientist, my friend. Soft skills are important even in quantitative work. Soft skills are what rock this job interview for you.

Of all the soft skills you could acquire, it’s your business skills that need strengthening. Remember that your customers are business owners. And as such, they need people who understand business. Only then can you generate insights that add value to your customer.

8. You don’t have meaningful relationships.

Do you want to get a job in the field but you don’t know any other Data Scientists? It’s time to crack, my friend.

Go to meetings. Join relevant groups on LinkedIn. Get to know people on Hackathons. Follow the right people on Twitter. Meet your fellow contributors on this GitHub project. Do something exciting!
As with any job search, 90% of your success does not depend on the breadth of your skills. It is determined by who can provide you with references and who can introduce you.

If your LinkedIn connections are limited to your mom and co-workers in that dead-end job, it’s time to personalize your profile. If your number of followers on Twitter is limited, tweet. If your blog has no readers, try SEO and cross-platform marketing.

Connections will come. But first you have to crack.

9. You don’t like dirty work

You’ve heard all the buzz around Machine Learning and Artificial Intelligence. You think data science could open the door to working with cutting-edge technologies.

Maybe you will. But I guarantee you won’t do it more than 5% of your time.

Once you’ve landed your dream job, you’ll spend most of your time cleaning data. Congratulations, you have just found a new job as a janitor!

If you don’t like it, go home – you shouldn’t be reading this article. If you still want to be a Data Scientist after reading all of this, it’s time you fell in love with dirty work.

Data science is not a career option. It’s a calling

Data Scientists are highly sought after individuals, which makes a lot of people interested in them. But to get a position in the field, dabbling is not enough. You have to work hard.

If you are still convinced to become a Data Scientist after reading this story, congratulations. You may be on the right track.

If at this stage you are not sure about becoming a Data Scientist, identify the main reasons for your doubts. Then start working on those points. You can do it!

This article was written by Ari Joury and was originally published on Towards data science. You can read it here.

Sean N. Ayres