5 Ways to Become a Data Scientist Without Work Experience
Resolving the “I need experience to get a job, but need a job to get experience” loop
With its 6-figure salaries and rapid growth, data science is a highly sought-after industry [1]. However, for most, landing that first DS job and breaking into the field is a major challenge.
It’s often the story of “you need experience to get a job, but you need a job to get experience.”
While experience and jobs go hand in hand, I don’t think this story is necessarily the case.
Meaning there are things other than past work experience that can make someone a desirable candidate for a data science role, and there are ways to get experience other than a job.
Toward that end, here I break down 5 ways to become a data scientist without having prior work experience.
1) Education
In data science, the needed technical skills often require additional training beyond the typical undergraduate program. This is why going for a graduate degree or enrolling in boot camp often helps those trying to break into the field.
From my experience, most of the data scientists I have met and worked with had some sort of additional education beyond an undergraduate degree. So while it's not a requirement, graduate degrees help you stand out as an applicant.
The downside of this, however, is that post-grad education costs money. So before defaulting to this, consider the 4 other options listed here.
2) Independent Projects
A cost-free way to get more data science experience is to pursue (interesting) independent projects. Not only is this a great way to learn, but it also gives you something to put on your portfolio and talk about in an interview.
The key is to curate interesting datasets which allow you to do interesting projects [2]. Two great ways to do this are one, building a web scraper, or two working with a business or organization that is willing to share their data for free analytics work.
This raises an important point that many people miss when it comes to independent projects. People tend to pack their portfolios with projects using toy datasets (e.g. MNIST, iris, etc.). While this type of project is fine for beginners who are just getting started, this is not something you want to put on a resume for a data science role.
1 real-world project is worth more than 10 toy projects.
However, this is much easier said than done in the early years. For those who are completely lost when it comes to self-directed projects, I see two additional options.
- Try to find existing open-source projects you can contribute to. This offloads the pressure of picking a killer project and also provides opportunities to learn from those with more experience.
- Try to find a mentor that can help give guidance on your project journey.
3) Content Creation
Another path toward a data science role that I’ve personally found helpful is making content.
This could be writing blogs on Medium that document new learnings, making videos on YouTube that walk through a past project, or anything else in that direction.
I find this is not only a great way to learn (teaching is the best way to learn), but it also gives you the opportunity to convey your competencies and personality beyond the typical resume or interview.
Additionally, content can sometimes take on a life of its own, and people may start reaching out to you with questions or job offers (I’ve experienced both).
4) LinkedIn
This option is becoming more and more popular these days, and it is simply to post about data science on LinkedIn.
I’ve seen people both in data roles and still in school building audiences on LinkedIn and, in turn, getting job opportunities.
The interesting point here is you don’t necessarily need to develop your data science skills further. Just simply convey the skills and experience you already have.
By posting often and growing an audience, you give your work and experience more visibility.
5) Freelance
This last option is my personal favorite, freelancing. I did this part-time while finishing up grad school, and it took my data science skills to the next level.
What this consisted of for me was finding contracts on Upwork. On Upwork, clients post work they need to be done, and freelancers submit proposals to do the work.
One reason I like this so much is it is a great way to get reps fine-tuning resumes, writing cover letters, and doing interviews. Given the smaller scope of contacts, in the time it takes to apply to 10 full-time roles, you can apply to 100 Upwork roles.
Additionally, if you do land contracts, you will get to work on real-world projects that provide value to your clients, and you get paid for it.
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[1] DS industry stats: https://www.bls.gov/ooh/math/data-scientists.htm
[2] Great advice on building a portfolio: https://medium.com/@jasonkgoodman/advice-on-building-data-portfolio-projects-c5f96d8a0627
