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Being on the job hunt is tough, there’s no two ways about it. Sending out resumes, re-writing cover letters, the interminable wait between applying and waiting to hear back (or just getting ghosted) – it’s not fun.
The good news is it’s a lot easier than it used to be. You don’t have to physically mail or drop off letters anymore; you can do a lot of applications with a few button clicks. There are plenty of specialized job boards, interview prep tools, and additional resources to make it more likely to find, apply, and actually get your dream data science job.
Let’s talk about the best free resources at your fingertips to get that data science job.
It doesn’t matter how shiny your resume is if you don’t have the skills to back up your credentials. One of the best ways to get data science skills is by doing your own projects.
It’s sometimes tricky to get ideas for data science projects, which is where Kaggle comes in. Kaggle hosts a huge log of datasets, machine learning competitions, and includes answers and different approaches for how to tackle various projects.
Source: https://www.kaggle.com/datasets
It’s an excellent resource because it allows you to apply your data science skills in practical scenarios, receive feedback, and learn from the solutions of others. Not only that, but if you actually win a Kaggle competition, that can serve as a bit of a flex to any employers. Most data scientists know of Kaggle and will be suitably impressed that you can tackle those problems.
In short, the most valuable asset Kaggle provides is real-world data and real-world problems. It offers valuable exposure to industry-level problems – and the opportunity to be noticed by top companies.
I may be slightly biased here as the founder of StrataScratch, but I founded the company because I noticed a real problem: it’s hard to prep for data science interviews. So I started collecting interview questions from as many different companies as I could and categorizing them by difficulty, type of question, and company. The result is a database of over a thousand real-life interview questions – both coding and non-coding – plus the solutions if you’re really stumped.
In my experience interviewing for data science jobs, it’s not just about having the skills, it’s also about being able to stay calm and think through whatever they throw your way. As you might imagine, it’s a lot easier to do that if you’ve seen the interview question – or some variation of it – before.
It’s a good idea to practice interview questions at every stage in your data science job hunt, too, not just when you have an interview lined up. Practicing IRL interview questions gives you a sense of what problems data science companies are interested in solving, as well as the skills you should focus on learning or honing.
Fun fact: while edX and Coursera have very expensive data science courses, you can get all the same knowledge absolutely for free simply by auditing the courses. Now, this means you don’t get a certificate of your accomplishments, which can definitely be valuable, but you do get world-class lessons, tutorials, and guides for free.
Source: https://www.edx.org/verified-certificate
Just find the course with the information you’re interested in, and sign up under audit mode. You can use this to shore up weak points on your resume, learn skills to do projects for your portfolio, or just explore a topic you’re passionate about.
You’re reading this on KDNuggets, so you should already know it’s a useful resource to get a data science job. KDNuggets doesn’t just offer blog posts, though. There are datasets (again, useful for projects), live and virtual events (great for networking), programming cheat sheets, and curated tool recommendations.
I’m throwing in Towards Data Science, too, since it’s another blog packed with tutorials, guides, how-tos, personal stories and experiences, and more. While some stories are paywalled, many are left free. You can easily browse the TDS homepage and look for free stories that don’t have a little star next to the author’s name.
In short, one of the best ways to get a data science job is to learn from other data scientists. Many of them are kind enough to post content online for free for you to read and enjoy.
Not sure where to start your data science job search? Classic contenders like LinkedIn and Indeed definitely win in terms of volume, but I love Wellfound to find data science jobs for the curated aspect.
Wellfound has a few advantages over other job boards. One, the filtering options are powerful. You can easily find jobs based on investment round, salary, equity, markets, company size, and more.
Two, it’s primarily startups. If you’ve tried and failed to get a FAANG job, it might be time to turn your sights to a different scene. Startups are hungry for data science talent, and if you can broaden your horizons to consider a slightly less conventional employer, you might have better luck.
Three, it’s just a bit newer and fresher, so I find it to be a better job-hunting experience. Features include telling you who invested in the company, how recently the recruiter was reviewing applicants, and pulling in stats from Glassdoor about leadership and life/work balance ratings.
Source: https://wellfound.com/
Job hunting is never fun, and it feels like this year has been worse in terms of companies ghosting more, making you sit through multiple rounds of interviews only to say the position was actually filled internally, or just straight up posting non-existent jobs to make themselves look better in front of potential investors. Perhaps you’ve even run into a scam job posting.
Hopefully, this list of free resources makes your life a little easier. With these five free tools, you’ll be better equipped to find and get your ideal data science job.
Nate Rosidi is a data scientist and in product strategy. He’s also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.
