So you have learned data science and you think you’re good enough to begin to monetize your skill, then you’re welcome. If you want to learn, check out our article on how to become a Data Scientist
While everyone looks to freelancing for soft skills like this, there are overarching possibilities and roles a data scientist can fit into.
And of course, not all roles may be suitable for you to monetize your data scientist skills. But with this article, you find the one that will resonate with your personality and speciality and then begin to push for it.
This article talks about the several ways your data science skills can be monetized provided you’re already skilled in the profession.
Who is a data scientist?
If you count yourself a data scientist, then you may well skip this. But again maybe you want to remind yourself who you are.
And so who is a data scientist? A data scientist is a professional who is skilled in collecting, analyzing and interpreting data to help drive accurate predictions, and decision-making in any establishment.
While the role of the data scientist is more than that, in doing that he combines several elements of traditional and technical jobs, like computer programmer, mathematician, statistician, and scientist. With these, he’s able to create algorithms that will drive the data analysis and prediction process. This is why data science also includes the use of highly advanced analytical techniques, such as predictive modelling, and machine learning as well as other applications of scientific principles.
So are you a data scientist or a data analyst? More than often, those who call themselves data scientists are data analysts because of the level of exposure they have in various aspects. So what are the vivid distinctions between a data scientist and a data analyst?
Difference between a data scientist and a data analyst
When an establishment wants to make sense of any data, not minding where it was collected, they always rely on data analysts to make sense of these data. What the data analyst does is clean this data, format it, analyze it and try to find trends that will help businesses make sound predictions and decisions.
This is where the Data scientist comes in. The data scientist is tasked with creating and developing the right algorithm formula or models that the data analyst must follow in trying to collect, sort and analyze information.
The data scientist is more of a programmer and a statistician. He uses statistics and other mathematics knowledge to create models and automation systems that support the work of the data analyst.
So who are you? Are you a data scientist or a data analyst? If you’re a data scientist, then it will interest you to know the various areas you can monetize this invaluable skill.
7 ways to make money using data science skills
1. Freelancing
Data science specialists can obtain a wide range of experience and get compensated for their skills through freelancing. You determine the terms and deadlines for jobs as a freelancer, making you your employer. It can be satisfying to have this flexibility and control.
Numerous websites, such as Upwork, Fiverr, Toptal, and LinkedIn, are available for finding freelancing work. To draw in clients, you can highlight your accomplishments and skills on these platforms as well as in your professional network. People will start contacting you with freelance opportunities as you develop your brand and portfolio. You can work with various businesses and industries and develop your talents by freelancing.
2. Consultancy
For local businesses, you can provide consulting services to enhance their modelling, data infrastructure, tooling, and analytics skills. or conduct online consultations with businesses throughout the nation and the globe.
Utilizing your experience, a consultant reviews existing company difficulties and makes strategic recommendations to improve data procedures. To identify problems and create a road map for progress, you can provide brief consulting engagements. can act as a continuous advisor to supervise the execution of longer-term projects.
Expert data science advice is in high demand, which makes consulting very profitable. Hourly prices frequently surpass regular salaries. By showcasing your specialized skills in particular areas, you can counsel across numerous industry verticals. Consulting allows you to grow your knowledge and exposes you to a variety of business difficulties over time.
3. Technical Writing
You may make a lot of extra money by producing technical content on websites like Medium and Substack. Within data science, you can select a specialized area to write about regularly. You can make money as you gain followers and an audience by charging for each article view in addition to receiving monthly payments from subscribers.
Just by creating material for these websites, many technical writers are making six figures. Companies in need of tutorials, blogs, documentation, and other materials may also post contract technical writing opportunities for you to apply for.
There’s a big need for high-calibre tech journalism, and your unique knowledge of data science can help you stand out. To eventually attract paying members, write frequently in the niche you’ve selected and market your content.
Technical writing can develop into a reliable source of extra money with the correct specialization and consistent publishing.
4. Participating in competition
Entering data science competitions can be a profitable side gig, even though it is not a reliable source of revenue. There are contests with reward pools ranging from sixty thousand dollars to five hundred thousand dollars and more on websites like Kaggle.
Your usual yearly pay may be equivalent to or greater than winning or placing highly a few times in a row. With the correct abilities, you can solve real-world machine learning issues and win thousands of dollars, albeit success is not guaranteed.
Comprehending the platforms and consistently engaging in competitions that correspond with your area of expertise is crucial. Although prizes from competitions shouldn’t take the place of a steady job, they can be a great way to capitalize on your skills if you’re confident in your data science abilities.
5. Collaborating on a project as a co-founder
Data specialists may be able to secure high-paying positions through collaborative project work. You might get offers to collaborate on open-source projects, startups, or commercial endeavours. Even though some partnerships are unpaid, many do pay well, particularly if you approach them like contracts.
Even if you are passionate about the project, bargain for just compensation when you are added to a team for your expertise. You can take part in paid collaborative data science initiatives on platforms such as Omdena.
For data experts, there are additional financial benefits to be gained by contributing to open-source projects on GitHub. Corporations sponsoring several well-known open-source data tools hire from the community of contributors.
You can gain recognition and be hired if you regularly provide high-quality code and documentation. Your ability is demonstrated by your open-source work. Active GitHub contributors can be directly sponsored by GitHub Sponsors in addition to possible employment opportunities. Relying users may provide financial support to well-known repositories. Your open-source reputation may bring in opportunities for contract work and consultation even in the absence of direct funding.
6. Career counseling
Data professionals who have worked in the sector for at least five years might make good money as a side gig by providing career counselling services. If you mentor recent graduates or anyone wishing to move into data roles, you can get paid by the hour.
You may charge for resume evaluations, career counselling, and practice interviews on platforms like Skilled. Private clients that are interested in individualized coaching to get into the industry can also be found.
You will share insider knowledge on the data science hiring and interview process during paid sessions. To become a strong candidate, you’ll suggest projects, classes, and other actions. Your practical knowledge of credentials and recruiting procedures is beneficial. Seeing people’s careers progress directly through counselling is another satisfying aspect of the work.
7. Being a data science tutor
Within data science, teaching is still a highly compensated profession. Platforms like YouTube, DataCamp, Udemy, Skillshare, LinkedIn Learning, and 365 Data Science allow you to develop an online course and make money from it. Alternatively, you may offer to teach a data science course as a guest lecturer at a university. To train future data experts, you can launch your academy or boot camp if you’re interested in educating continuously.
Teaching as a side gig can be a terrific way to make a great living by sharing your knowledge through planned sessions and programs, either in person or online. Students will be keen to learn from seasoned professionals because data skills are still in great demand. Setting your hours and reaching a global student body eager for practical data science knowledge are two benefits of teaching.
Conclusion
Data science as a profession holds great promise today. But its potential and usefulness are just emerging. The future and potential for data science is so enormous given the emergence of technologies like the Internet of Things and the world of artificial intelligence. We live in a world where everything is data-driven.
The data scientist who is well-read in his profession will benefit optimally from this yet untapped gold mine. And to say the least, the opportunities for a data scientist are enormous.
