The nature of doing jobs in the current modern world has transformed drastically due to the COVID-19 pandemic. Alongside an individual’s full-time job, several people now have the availability as well as the flexibility to take on side hustles that offer an additional source of income.
Data science is an emerging field. It is also among the fast-paced growing industry that provides exciting opportunities for many individuals. The high demand for data scientists makes this field lucrative. The field of data science is comprehended to many advanced concepts like the internet, Artificial Intelligence (AI), Machine Learning (ML), and many more. Many industries are looking for talented data scientists who have at least one data science certification, as their role is quite essential because they offer businesses and organizations to manage large chunks of data without any hassles.
According to a report shared by the U.S. Bureau of Labor Statistics, data science expertise will drive a 27.9 percent rise in employment in the field by 2026. The need for professional and qualified individuals in this is very high. In fact, there is a shortage of data scientists.
Firstly, let’s go through the advantages and disadvantages of being a freelance data scientist.
The best part about having a freelance career in the field of data science is that one will get to work with people from all over the globe. The opportunities are endless, and one can learn to look at a problem from many different perspectives to solve it in a better way.
A freelance data scientist also gets to pick the desired types of projects to work on — something that isn’t always possible when they have a full-time job. Also, as a full-time employee, one can only get to work in a single industry. Whereas for a freelancing career one can experience every project and gain innumerable knowledge on various domains.
When a freelance data scientist works on a variety of tasks in many different domains, their portfolio grows too. Apart from this, doing a data science certification will give an individual better exposure as well as larger scope to get better projects.
A freelance data scientist isn’t stuck with a single way of doing things and can adapt quickly to new workflows. Their capacity to learn will improve followed by their productivity.
There are a few downsides to becoming a freelance data scientist. One thing is that there are a limited number of freelance data science jobs available in the current global market.
It is usually mid to large-sized organizations that hire data scientists, and these organizations tend to hire full-time employees rather than freelancers. There is a higher demand for freelance web developers/designers as compared to data scientists.
A freelancing career also doesn’t ensure job security, and one needs to actively be on the lookout for new projects. Due to this, it is a good idea to keep the full-time job while taking on freelance projects, especially when one is in the starting stage.
Essentialities that are required to be a freelance data scientist
To build an appealing freelance career in data science a person should enhance their skills. There are many best data scientist certifications that offer deep insights to every individual to be a professional freelance data scientist. Knowledge of the following will help a person in many ways:
Prioritize the skills based on the project
The client will offer a certain kind of criteria before handling the project to a freelancer. It will be a huge benefit if a person shapes their CV based on the project, for which they have applied. For instance, the requirement for the project states that a person must possess skills in Python and ML. But one can possess the knowledge of Python and deep learning. And, then they must mention all the details and projects, which they have done in Python and deep learning. And miss out on the ML details and projects. This might result in losing the chance on the applied job. So, mention that you have knowledge of Python and ML and also have very good practical exposure to deep learning. Try to learn it in the meanwhile at least the basics.
Showcase all the data science-related projects
Having practical knowledge will always dominate theoretical knowledge. Stating clearly about all the data science-related projects, which a person has worked on, skills one has attained, and data science certification one has done will make the client feel impressed, and chances of getting the project will be more.
Mention the GitHub profile
It has become quite important to have a profile on GitHub for those who want to take up a freelance project in the area of data science. The profile on GitHub will be useful for the client to check the projects that a person has worked on. They will also feel more reliable about the skills they possess; it is a quick and simple way to impress the clients and grab the project.