Some tips on how you can make your non-traditional background a selling point for your first data science job
Data science is one of those exciting 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 conclusions. from data to C-level executives.
No matter what experience you have, you are welcome in data science.
2020 became a year where people began to teach themselves how to code and transition to technology from a variety of backgrounds and the trend looks set to continue in 2023. While a non-traditional career or educational background can make it difficult Getting into data science isn’t impossible when you know how to leverage your existing skills to complement your new data science skills.
None of the advice shared here is revolutionary or life-changing; instead, they are tried and true tips that I have personally found to work when 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 in a new career in data science.
You’ve learned the skills of data science, now you need to show what you can do.
Your portfolio is everything when applying to data science jobs, not just as someone with a non-traditional background, but also for those who have officially studied it. Portfolios are often the make or break of a recruiter’s offer to come in for your first interview, so it should be an impactful resource filled with your best work and showcasing how your non-traditional background makes you more of a candidate. stronger than the rest. .
Most data science portfolios are created and hosted on GitHub, an industry standard where you should store all your personal data science projects. You can learn how to create a GitHub wallet here:
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 information. Insight is everything in data science, so you should play to your strengths when choosing projects.
For example, if you were a nurse, projects in your portfolio might focus on highlighting how a hospital could improve its efficiency, how doctors could use AI to make more accurate diagnoses, or how worsening environmental conditions are proportionally increasing rates of hospitalization. Same goes if I were a teacher: how could more education-oriented kids’ TV shows help kids get a head start on the things they need to become well-rounded learners?
Whether you want to be a data scientist in your previous industry or not, it’s vital to demonstrate that you can apply what you already know to solving problems using data analysis. These projects should try to solve the problems that he encountered while working (or that may have led him 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 am currently working on a personal project looking at how the probability of finding missing persons for my area can be better standardized. I work in search and rescue, and while no two calls are the same, it is possible to find correlations between the types of calls and where you can expect to find people. In other words, take a very real problem that exists, break it down into its components, and determine 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 you apply for, it will certainly show that I can solve a problem using my data science skills, which in the end, is all an employer is looking for anyway. .
key takeaway
- Create 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 a real world problem into something that can be solved using the data science. .
Your first experience in data science will be different for everyone. For me it was a volunteer, while for others it could be a freelance job. For some of the writers here at Into Data Science, they started by sharing their experience through articles.
Getting some experience in simple data science is a great way to get hands-on experience working with data in a real-world scenario. These may not be paid opportunities, but they will pay off in the future when you are hired as a data scientist.
The simple data science experience might resemble creating an Excel sheet that could predict your parents’ future monthly expenses based on historical price data. Or, it might look like doing customer research for a local online business and helping them market their top-performing products. Or, it might look like building a dashboard for a social media marketer to determine how customer enrollment matches Google search trends. If you really want to tap into your non-traditional background, pursue data-related experience in that field and demonstrate how your practical industry skills, coupled with your new technical skills in data science, help you produce even more information than you ever could. have with a single skill set.
Whichever the case, you should be looking to complete 3-4 simple, real-world projects that show potential employers that you have the technical skills they need (and the non-traditional experience to provide more insight than most). Most companies want their data scientists to get going (to varying degrees), so it’s a good idea to get the basics practicing through these simple projects.
These projects can be featured as work or volunteer experience on your resume. Also, you should look to get a testimonial from the person or company you did the project for, which could be used as a reference or as one more reason why a company should hire you. Most important of all, these simple experiences show employers that your non-traditional background is complementary to your skills as a data scientist.
For example, I used my last college project as experience for a career in technology. It was a great talking point with prospective employers and gave them great insight into my skills, both technical and transferable. By showing them that you had worked as part of a team to create a tangible result for a large client company, they could trust that you would deliver the same results with “real life” work. Also, while the job wasn’t entirely job related, it did show that I had transferable skills and a greater understanding from my less traditional background.
key takeaway
- Get a simple data science experience by volunteering your time, doing an internship, 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 skills as a data scientist.
One of the best advice I have received from Masters and PhD. 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, creative thinker, able to work in a fast-paced environment, and problem solver. Or, if he were a teacher, he is a great communicator, can break down complex topics into simple statements, is a problem solver, and is diligent about meeting deadlines. All of these skills are valued in the data science industry and should be featured lavishly.
What you will find in good tech companies is that they will hire a data scientist who has all the essential soft skills (some of which are listed above) even if they don’t have a perfect technical background because they know they can train for technical skills, they cannot train for soft skills.
For example, I remember when the company I was working for hired a developer who also wanted to work in data science. Although the developer did not have perfect data science skills, the company hired the developer because they knew 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 fully transition into a data science role if the time came, or they could continue to work as a developer and receive additional training.
key takeaway
- Transferable skills are what will set you apart from other candidates – highlight the ones most relevant to the position and discuss how what you learned in your previous experience applies to a data science position.