Data science is one of those interesting tech fields where you can talk to people who once studied philosophy, worked as nurses, or walked dogs for a living, and are now scraping the web, building machine learning models, and presenting data conclusions to C-level executives.
It doesn’t matter what background you have, you are welcome in data science.
2020 became a year where people started to teach themselves how to code and transition into tech from a variety of backgrounds and the trend has appeared to continue into 2023. While a non-traditional career or educational background can make it challenging to get into data science, it’s not impossible when you know how to leverage your existing skills to complement your new-found data science skills.
None of the tips shared here are revolutionary or life-changing – instead, they’re tried and true tips that I’ve personally found to work while trying to break into data science from a non-traditional background. Your data science skills and past experience will speak for themselves, you just need to use these tips to leverage them into a new career in data science.
Build a portfolio
You’ve learned the data science skills, now you need to showcase what you can do.
Your portfolio is everything when you’re applying to data science jobs, not only as someone from a non-traditional background but also for those who have studied it officially. Portfolios are often what make or break a recruiter’s offer for you to come for your first interview, which is why it needs to be an impactful resource full of your best work and demonstrating how your non-traditional background makes you a stronger candidate than the rest.
Most data science portfolios are built and hosted on GitHub, an industry standard where you should store all of your personal data science projects. You can learn how to build a GitHub portfolio here:
How to Create a Professional Portfolio on GitHub That Will Help Land Your First Job in Data Science
When entering data science from a non-traditional background, you want your portfolio to show that you have transferable skills from your previous experience that make your projects full of unique insight. Insight is everything in data science, which is why you need to play to your strengths when choosing projects.
For example, if you were a nurse, your portfolio projects could center around highlighting how a hospital could improve its efficiency, how doctors could use AI to make more accurate diagnoses, or how worsening environmental conditions are proportionately increasing hospital intake rates. The same thing goes if you were a teacher – how could more education-driven children’s TV programs help children get a head start on the things they need to become well-rounded students?
Whether you want to be a data scientist in your previous industry or not, it’s vital to show that you can apply what you already know to solve problems using data analysis. These projects should take a crack at solving problems you encountered while you were working (or that may have driven you to leave that position) using data sets, statistical analysis, machine learning, and Artificial Intelligence.
Additionally, these types of projects demonstrate your ability to break down a real-world problem into something that can be solved using data science.
For example, I’m currently working on a personal project that looks at how the probability of finding missing persons can be better standardized for my area. I work in search and rescue, and while no two calls are the same, it’s possible to find correlations between the types of calls and where you can expect to find people. In other words, taking a very real problem that exists, breaking it down into its components, and determining how missing person data can be used to make search and rescue operations more efficient. While this may not be perfectly relevant to the next Data Science job I apply for, it will certainly show that I can solve a problem using my data science skills – which in the end, is all that an employer is looking for anyways.
How to Effectively Showcase Personal Projects on Your Data Science Resume
Key takeaway
- Build projects for your portfolio that solve problems you encountered in your previous industry – this shows potential employers that you are dedicated to finding better ways of doing things and that you can break down a real-world problem into something that can be solved using data science.
Get simple data science experience
Your first data science experience will look different for everyone. For me, it was volunteering, whereas for others, it could be freelance work. For some of the writers here on Towards Data Science, they got their start by sharing their expertise through articles.
Getting simple data science experience is a great way to get some hands-on experience and work on data in a real-world scenario. These may not be paid opportunities, but they will pay off in the future when you get hired as a data scientist.
Simple data science experience could look like building an Excel sheet that could predict future monthly expenses for your parents based on historical price data. Or, it could look like doing a customer analysis for a local online business and helping them market their highest-performing products. Or, it could look like building a dashboard for a social media marketer to determine how client enrollment coincides with Google search trends. If you really want to leverage your non-traditional background, seek out data-related experience in that field and demonstrate how your practical skills in the industry, coupled with your newfound technical skills in data science, help you produce even more insight than you might have with just one skillset.
Whatever the case, you should look to complete 3–4 simple, real-world projects that show potential employers that you’ve got the technical skills they need (and the non-traditional background to provide greater insight than most). Most companies want their data scientists to hit the ground running (to differing degrees), so it’s a good idea to have the basics down by practicing through these simple projects.
These projects can be showcased as work or volunteer experience on your resume. Additionally, you should seek to get a testimonial from the person or company you did the project for, which could be used as a reference or as just one more reason why a company should hire you. Most importantly of all, these simple experiences show employers that your non-traditional background is complementary to your abilities as a data scientist.
For example, I used my final university capstone project as an experience toward a career in tech. It was a great talking point with potential employers and gave them a great idea of my skills, both technical and transferable. By showing them that I had worked as part of a team to create a tangible result for a large client company, they could have confidence that I would deliver the same results with "real-life" work. Furthermore, while the work wasn’t completely related to the job, it showed that I had transferable skills and increased insight thanks to my less traditional background.
Key takeaway
- Get simple data science experience by volunteering your time, interning, freelancing, or sharing what you know on social media. This shows employers that you have the technical skills required for the job and reassures them that your non-traditional background is complementary to your abilities as a data scientist.
Highlight transferable skills
One of the best pieces of advice I’ve received from Master’s and Ph.D. students looking for jobs outside of academia is that you have numerous transferable skills from whatever educational or occupational background you have. While it may not seem like it, think about it for a second.
For example, if you were a nurse who is now transitioning into data science, you are highly organized, detail-oriented, a creative thinker, able to work in a fast-paced environment, and a problem-solver. Or, if you were a teacher, you are a great communicator, you can break down complex topics into simple statements, you are a problem-solver, and you are diligent about meeting deadlines. All of these skills are valued in the data science industry and should be highlighted profusely.
What you will find at good tech companies is that they will hire a data scientist who has all of the essential soft skills (some of which are listed above) even if they don’t have a perfect technical background because they know that they can train for technical skills – they can’t train for soft skills.
For example, I remember when the company I worked for hired a developer who also wanted to do some work in data science. While the developer didn’t have perfect data science skills, the company hired them because they knew that the person could work as a developer while the company trained them in relevant data science skills. It would then be possible for the person to transition completely into a data science role if the time came, or could continue working as a developer and get trained on the side.
Key takeaway
- Transferable skills are what will set you apart from other candidates – highlight the ones most relevant to the position and address how what you learned in your previous experience is applicable to a position in data science.
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