How to Get a Job in Data Science (2023 update)
Data science is a rapidly expanding field. In fact, the U.S. Bureau of Labor Statistics expects the field to grow by 36% through 2031. Since it impacts so many other industries, there are endless career opportunities.
But getting started can be hard. If you don’t come from a data science background, you might wonder where to begin.
You’re in luck. We’ve put together a step-by-step guide on how to get a job in data science. Let’s get started.
Improve Your Technical Skills 2023 Update
The first step before you even begin looking for a job in data science is to build the requisite technical skills. Luckily even if you don’t have a programming background, there are still plenty of resources to learn from.
Make sure to choose a programming language and learn enough to become comfortable with it. It would help if you also learned about exploratory data analysis and machine learning algorithms you might need to deal with.
It might seem unimportant, but communication is also important in data science. Work on sharing your thoughts and ideas in a clear manner.
Work on Projects
Once you feel comfortable with your technical skills, it’s time to work on projects you can use to showcase your skills. These projects don’t need to be overly complex. A prediction model or recommendation system is enough to start with.
If you need a source where you can learn to build projects, there are various good places to start. You can learn the most commonly used standards and techniques straight from the best data scientists in the world.
Besides allowing you to show off your skills, building your portfolio is also a great way to improve your skills further and stand out from your competition. You’ll need to work through real-world issues you’ll eventually see on the job. This allows you to learn more in-depth than what you’ll see in simple tutorials.
If you’re looking for a place to find real problems to work through, a few platforms and communities can help with this. Some even allow you to collaborate with others on projects that have a positive impact on the world and the environment.
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Build Your Portfolio
Once you’ve completed a project or two, it’s time to build a portfolio. This will serve as a digital resume and help you stand out from the crowd. It’s common for data science openings to receive a lot of applications. Anything you can do to push yourself to the forefront is helpful.
Luckily, it’s relatively simple to put together a portfolio website. You don’t need special skills and can create one in only a couple of hours.
Write About Your Work
Writing about your work will help others understand what you do and build confidence from the recruiters. It’s common for human resources to do the initial screening of applicants. Since these people may have a limited technical background, writing can help them understand what you’ve been doing.
Since you’ve already made a portfolio website, adding a blog section will be very easy. However, you can also write in the read me section of GitHub. These don’t need to be expansive and overly complex posts. But enough to explain your project to those who might not understand it would be helpful.
Build Your Resume
You’ll also need a traditional resume for most positions and your portfolio. Make sure to list relevant experiences. Remember, it doesn’t necessarily have to be from a job. Volunteer work and study projects may also be worthwhile to include.
It is very common for organizations to use software to sift through the first round of resumes. Make sure to optimize it as well as you can. Include keywords that are in the job description, and make sure it’s formatted well.
Having someone else look over your resume is a great way to ensure it’s up to par. Your local library may even have resources to help you.
Network and Find a Mentor
In the world of data science, who you know can make a huge difference in how easy it is to get a job. In fact, many organizations start the hunt by asking their current employees for recommendations. Knowing someone can make the process much easier as you’re just starting out.
Sometimes that’s easier said than done. Luckily there are ways to network. Start by seeing if there’s a meetup in your area. If you need to, you can always start one yourself.
Conferences and other events are also great ways to network. These also help you see how data science helps businesses in your area.
If you can find a mentor, you also essentially gain their network. Plus, a mentor is a great way to get feedback throughout your career.
Start the Job Search
Once you’ve prepared, it’s time to start the job search. You might be tempted to apply to large companies you’ve heard of. But there are better approaches than this. Usually, these positions are very difficult to get if you don’t have much experience.
Instead, aim to apply to smaller, growing companies. Not only are these usually easier to apply to, but smaller companies also provide growth potential. You’ll gain visibility from the start and a unique experience that can help you further down the line.
It’s also very easy to get caught up in the type of job you’re looking for. Many people want a data science role but forget that data-related roles could be just as good. These jobs could include business analyst roles, data analyst roles, or other positions that focus on working with data.
This type of position will help you improve your domain knowledge and your data skills. Plus, it’s a great way to put your skills to work to learn data science.
If a data-related job comes first, don’t be afraid to go after it. It’s usually very easy to switch to a data science job when starting with a data-related job.
Find Your First Job in Data Science
Finding your first job in data science can be overwhelming. Start by building your skills and then creating ways to showcase them. If you look for the right support and types of jobs, you’ll have a better in into the field. This way, you’ll have an easier time finding a position you love.
In as little as six months, you can be on your way to a new career in data science! Learn more about our data science course.