The technology and data science industries have been built on magical stories of geniuses who made millions without graduating college. With examples from Zuckerberg to Gates and everyone in between, it seems like a degree is one of the last things you need to become a success in Silicon Valley (or anywhere, for that matter). But how do you get a data science job without a degree?
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- Be willing to put in the effort
Possibly the most important thing on this list (which is why we put it first) is that you have to be willing to put in the hard work and time to get where you want to be. Chances are, you don’t have a trust fund that can do all the work for you, and maybe you’re not a child prodigy like Zuck was, but that doesn’t mean you can’t be successful. It just means you have to work, and you have to work hard.
Skipping college can be a great way to save money, get started on your career earlier, and prove you know the direction you want your life to take, but it also means you have nothing but your portfolio to show employers you’re worth hiring. Degrees act as a signal to employers that you, at the very least, know how to take direction and understood the material well enough to pass. That signal makes their job in hiring easier, so if you want to stand out against that you have to prove to them early on that you’re just as qualified (if not more) than that other candidate.
- Compete in Kaggle Competitions or Participate in Hackathons
You should be doing quantifiable things that you can put on your resume and prove that you have the experience it takes to secure a position. Kaggle Competitions and Hackathons (check out this site to find a Hackathon near you) are both great places to start.
Competitions like these are great because (if you’re the procrastinating type) they give you a deadline to prepare for, they let you hone in your skills, they can show you what skills you’re lacking or need to brush up on, and give you some in-the-moment stress that could mimick being on deadline for a big project at the company. All of these advantages are things you can explain on your resume and will show employers that you’re not just sitting around waiting to be handed a job.
- Constantly look out for new places to learn
The next thing to remember is that, just because you’re not going to formal education to get a degree, doesn’t mean you aren’t learning. If anything, choosing to not go to college means you should be working at your learning even more. You could go to conferences (ODSC Europe and West just finished, but East is coming up and you can check out our recaps of the conferences here on Open Data Science!), take free courses (Amazon and Google both have some great ones), or do your own projects and keep them updated on your GitHub. But in any case, you should be working and learning about how to do the work you want to get hired for.
This comes into play in proving that you’re qualified for the job: you should always be developing your skills and updating your resume with the new information you’ve learned. Also make sure you stay up-to-date on the latest industry news. In a college classroom, you would often get notified of important events by a professor or your peers, but without that support system, you should be seeking out the news yourself. We have a new news section of the website here that gets updated weekly, but you can also follow Data Science news on Medium, or keep your eyes scanning on Arxiv.org (or Open AI or Deep Mind) for the newest papers—or just keep your eyes out for our monthly paper round ups.
- Search actively
While on the search, you should be looking actively not passively for a position. This means turning on news notifications for your key words, this means looking at job sites weekly if not daily, it means applying for all the jobs you can find that fit what you want to do, and it means putting maximum effort into each of those applications (write your cover letter, send them follow-up emails, do research on each company, etc).
You should also be following companies you’d like to look for on social media and LinkedIn, and networking with anyone you can (those learning opportunities from above sometimes offer really great networking). If you’re on Indeed, look for the tools and frameworks you’re familiar with, rather than just umbrella terms like machine learning. You might be a specialist in a rare framework
You have to put yourself out there far more than anyone with a degree would have to, but it can be far more rewarding to put in the extra work and spending the time searching actively can end up being the difference that gets you your dream position.
- Know that your first job title might not be what you expect
An unfortunate part of getting a data science job without a degree is that you might have to start with a different job title than you were hoping for—you might not land a data scientist role, but you can get something that will eventually put you in that position. This isn’t always the case, and if you do a good enough job proving that you’re qualified, you could avoid it. However, employers are allowed to pay you less based on college experience, and they may be hesitant to put trust in your work if this is your first job and you’re entering without a seal of University approval. They might want you to start with some grunt work—like data cleaning or data labeling—to prove you’re trust-worthy (but companies also do this to freshly post-grad employees who don’t have much work experience)
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If you feel this is happening to you in your job search or when you’re hired, schedule a meeting with your supervisor and discuss your options. Ask about milestones they’ll want you to hit within the first six months and first year of working at a company, discuss what specific characteristics and actions they’re looking for in your role, and find out what the normal basis for promotion is. That way, you can focus your efforts on hitting those goals and proving your worth, so you can begin salary negotiations with tangible successes you’ve had that equalize your value to that of someone with a degree.