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Over the past decade, freelancing has become more and more popular — and for good reason. In fact, the World Economic Forum notes that 16.1% of the EU workforce is comprised of freelancers, which translates to around 36 million Europeans. Many cite flexibility as a major benefit to freelancing, as well as a healthier work-life balance, higher income, and the ability to work wherever (and whenever) they want.
Therefore, it comes as no surprise that this market is growing rapidly. And if you’d like to jump on the bandwagon as a data scientist, then it is important to understand that it doesn’t come without its challenges. You’ll also need to know how to set yourself up for success and have an edge against competitors, so we’ve outlined a few ways on how you can start successfully freelancing as a data scientist:
Present yourself in the correct manner
The first step to presenting yourself well is to have a portfolio that displays your best achievements. Consider having a website that introduces who you are, what you’re an expert in, what you’ve accomplished, and how to contact you. Then, let potential clients find you by listing yourself on freelancing directories, as updating your header on LinkedIn simply isn’t enough. Join websites such as UpWork or Toptal, and try to develop a presence on the web as an expert in your field.
Learn how to manage your time
When you’re working at home or on your own time, you need to find the motivation to work at odd hours. Freelancing generally means you do everything: administrative duties, finding new work, and meeting various deadlines all at the same time. Because you probably won’t have a manager to remind you what you have to do, it’s important to be able to manage your own time efficiently.
Create a schedule that will get the best out of you — whether that’s the usual 9am to 5pm or 5pm to 11pm. Follow a routine so that you can get motivated and get into the mindset of working at home, or consider working in a coworking space for a more conducive environment.
Be an expert at the basics and brush up on your skills
Knowing the basics is vital, and you’d be surprised at how many people still have to train themselves on the fundamental tools of the trade. For instance, one of the most basic programming languages every data scientist should know is Python, as it can be used in a variety of fields. From web development, game creation, and even for AI purposes, Python is one of the best all-around languages for data scientists.
Of course, there are more languages to learn, such as R and SQL. Brush up on necessary skills in your downtime, so you can expand your portfolio of knowledge and expertise in the long run.
Find a niche
One of the best ways to remain competitive is to find a niche. Technology is ever-changing and constantly growing, so it’s impossible to be an expert at everything. The two million-strong data science community on Udemy is a testament to this constant growth, as it is home to courses on Python, machine learning, statistics, and data analysis, just to name a few. All these are topics that you can choose to focus on, as having a niche makes you an expert at what you do — and ultimately more valuable for companies who want to hire you.
Network with fellow freelancers
The internet is packed with reports and knowledge, but The Story of Grip explains that the best way to learn is through like-minded individuals. Join forums and communities where you can share ideas, collaborate, and learn from others. By doing this, you will establish yourself as a freelancer who can give valuable insight, which employers will surely notice.
Being a successful freelance data scientist requires a lot more than just quitting your job and changing your LinkedIn profile. Starting out as a freelancer is tough, and clients won’t flock to you right away. Grow your skills and knowledge, and do your best to have an edge over other freelancers — whether it’s through a superior work ethic or having a good niche.
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