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KDnuggets Home » News » 2021 » Feb » Opinions » How to Get Your First Job in Data Science without Any Work Experience ( 21:n06 )

Platinum BlogHow to Get Your First Job in Data Science without Any Work Experience


Creativity, grit, and perseverance will become the three words you live by.



By Madison Hunter, Geoscience BSc Undergrad Student

Whether you’re a new graduate, someone looking for a career change, or a cat similar to the one above, the data science field is full of jobs that tick nearly every box on the modern worker’s checklist. Working in data science gives you the opportunity to have job security, a high-paying salary with room for advancement, and the ability to work from anywhere in the world. Basically, working in data science is a no-brainer for those interested.

However, during the dreaded job search, many of us run into a situation similar to this one:

Job Experience Catch-22

Image by 9GAG

 

Yeah, that looks pretty familiar.

Having run into many situations myself where companies are often looking for candidates with 20 years of work experience before the age of 22, I understand the aggravation that comes with trying to look for a job when you’re a new graduate, someone looking for a career change, or even a cat, with no relevant work experience.

However, this is no reason to become discouraged. While many data science jobs require work experience, there are plenty of ways to create your own work experience that will make you an eligible candidate for these careers.

All you need is a little creativity, grit, and perseverance.

 

It’s not about what you know. It’s about who you know and who knows you.

 
In countries similar to Canada where having some form of university qualification is becoming the norm (in 2016, 54% of Canadians aged 25 to 64 had a college or university certification), it’s now no longer about what you know. Instead, it’s about who you know and who knows you.

Google “the importance of networking”, and you will be flooded with articles from all the major players (Forbes, Huffington Post, Indeed, etc.) on why networking is one of the most important things you can do for your career. Forbes says it best:

“Networking is not only about trading information, but also serves as an avenue to create long-term relationships with mutual benefits.” — Bianca Miller Cole, Forbes

 

While networking is a phenomenal way to get insider knowledge on how to become successful in a particular career, it can also serve as a mutually beneficial relationship later on down the road.

I got my first job in tech by maintaining a relationship with a university colleague. We met as a result of being teamed up for our final four-month-long practicum. After graduation, we kept in touch. Almost two years later, I got a message saying that the company they work for is interested in hiring me to do some work for them. Thanks to maintaining that relationship, I managed to score my first job after graduation with no work experience thanks to my colleague putting my name forward.

In other words, it’s important to make a few acquaintances while you’re going through university, to attend networking events and actually talk to people there, and to put yourself out there so recruiters begin to know your name.

 

Become a writer and contribute to a personal blog or a major publication.

 
Data scientists are natural storytellers thanks to their ability to turn massive data sets into compelling visualizations that tell stories to the masses. Because of this, it only makes sense that aspiring data scientists should write about their work to demonstrate their communication skills to future employers.

Many data scientists have touted the benefits of starting a blog or writing on a platform like Medium. Despite what many say, the benefits of writing don’t stop at making you a happier, more stress-free person — writing will also help your data science career.

As I mentioned above, being a storyteller and an overall solid communicator, are essential skills of data scientists that only improve when they’re being practiced. For example, by explaining the results of your data analysis to the general public, you begin to think of data in simple terms that anyone can understand and appreciate. As Richard Feynman once said, “I couldn't reduce it to the freshman level. That means we really don’t understand it.” Not only will writing make you a better communicator, but it will also give you a deeper comprehension of data science concepts, thus making you a better data scientist.

However, the benefits of writing don’t stop there.

As a future data scientist, articles you’ve written become part of your professional portfolio and give recruiters insight into your comprehension of particular concepts. Not only will they be able to see that you’ve been able to build a following of people who trust and value your work, but they will also be able to see that you’re willing to contribute knowledge to further the lives and careers of fellow data scientists. Furthermore, publishing on a website that pays you for your work tells recruiters that people value your knowledge so much, that you’re actually getting paid for it.

Here’s a couple of resources to get you inspired to write:

Why Data Scientists Should Write Books, And Why I Did.
The knowledge is out there.
 

Practical Advice for Data Science Writing
A few useful tips to get started writing about your data science projects
 

Become a freelance data scientist and build up your own consulting business.

 
The Marines said it best: improvise, adapt, overcome.

Instead of constantly fighting an uphill battle, go with the flow, and create your own data science consulting business.

I know from experience how discouraging it is when you’ve sent off a hundred resumes only to get rejection letters and radio silence in return. So, if no one will hire you, hire yourself!

Freelancing is easily one of the most terrifying things people can do to make money, and it’s definitely not for everyone. However, it’s a fair alternative to banging your head against a wall for days on end waiting for potential employers to get back to you (or not).

If you have the skills and the confidence, why not take on some freelance clients? It’s a win-win situation. You get real-world experience without having to go through the pain and suffering of the hiring process (mind you, there can be just as much pain and suffering doing freelance work which is why it’s not for everyone). The beauty of hiring yourself is that if you finally get a job offer from one of your coveted companies thanks to the real-world experience you’ve been able to accumulate, you can walk away from freelancing at any time.

But who knows? Maybe you’ll end up really enjoying the freelance life. In my opinion, it’s worth the gamble if you’re unable to find work the conventional way.

 

Work on your own projects to showcase your talents.

 
If you asked me for a definition of “data science”, I would sum it up as being an interdisciplinary field that focuses on solving problems and gathering information. Therefore, it makes sense that an employer wouldn’t want to hire anyone who hasn’t solved any problems or who hasn’t been able to draw any conclusions from a data set.

By creating your own projects, you show employers that you have that innate curiosity and drive that is required for data scientists to be successful in their work. Not only that, but many employers in tech request to see your project portfolio so they can see the quality of your work before they hire you.

It’s now easier than ever to find free data sets to build projects on. Think I’m kidding? The last time I checked, there were 67,862 data sets available on Kaggle for anyone to use. That’s a lot of data.

Furthermore, a quick search will lead you to hundreds of articles full of different data science projects to lend you inspiration. Here are a few to get you started.

The 7 Data Science Projects I Plan on Completing in 2021
How I plan on using these projects to improve my data science skills by the end of the year.
 

12 Data Science Projects for 12 Days of Christmas
Relevant and valuable data science projects that you can do in a day!
 

12 Cool Data Science Projects Ideas for Beginners and Experts
“How many data science projects have you completed so far?”
 

A Guide To Getting Data Science Projects Ideas
How to come up with self-study, portfolio or business ideas. From someone with too many.
 

Intern, volunteer, or do pro bono work to get valuable industry experience.

 
Sometimes, the best way to get the necessary work experience is to do the work for free. No one likes to work for nothing, but in a world that often requires you to have 20 years of work experience before you’re 22, working for free is often your ticket to job-hunting success.

Interning, volunteering, or doing pro bono work, are three of the best ways to get the necessary work experience that many companies are looking for. Not only do these “jobs” allow you to gain real-world experience using real-world data, but it also shows hiring managers that you’re a team player who earned their work experience the hard way without pay. Furthermore, you might get the opportunity to create meaningful solutions that will positively impact many individuals and communities along the way. If the company you work for is willing to compensate you with a glowing review on your LinkedIn profile or a reference letter, even better!

 

Final thoughts.

 
For anyone entering a new field, be it a fresh graduate, someone seeking a career change, or even a cat who learned to type, having a lack of work experience can be a daunting situation to overcome.

However, there are tons of opportunities out there for you to gain work experience as long as you’re willing to take them on. Fortune tends to favor the brave, and that isn’t more true than for people looking to make it in a new field.

By practicing a little creativity, grit, and perseverance (and also maybe some patience), you’ll be well on your way to landing that first coveted job in data science.

 
Bio: Madison Hunter is a Geoscience BSc undergrad student, Software Dev graduate. Madison produces ramblings about data science, the environment, and STEM.

Original. Reposted with permission.

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