Introduction
For most freshers, landing a Data Science job seems like a chicken-or-egg situation. You need to have hands-on work experience to get selected for such a job, but how do you get any work experience without first being hired?
By now, you must have heard, read or seen a lot about the scope for immense growth that a Data Science career can offer. However, for many aspiring Data Scientists, the reality appears to be hard-hitting.
The career potential of a Data Scientist is undoubtedly very rewarding once an individual gets the job, but getting the job without prior work experience is the main obstacle they face. Below, we examine some practical solutions to this dilemma:
Work on personal Data Science projects
This is an interesting and highly practical way to gain real-life Data Science experience. Once you finish a project, you can showcase your work on a platform like GitHub. Focus on small projects, and try to demonstrate important Data Science skills in your efforts.
The advantages of working on your own project are that you gain hands-on experience in generating ideas, collecting data, cleaning data, analysing data and building predictive models.
Therefore, you gain a comprehensive understanding of the entire process. As far as possible, try to script clean codes and develop clear visualizations that potential stakeholders can find easier to follow.
Do not attempt to display too many skills at once, as you might end up unnecessarily complicating matters for your audience. Simple and small projects will illuminate the core skills you wish to draw attention to. For example, consider obtaining a complicated datasheet and cleaning it up. This simple project will demonstrate your prowess in:
- Scoping a data project and formulating a suitable plan
- Gathering data using different collection methods
- Contemplating different data cleaning methods and choosing the most suitable one
- Handling different data formats such as XML, CSV and JSON
Contribute to open-source projects
The best way to enhance your coding skills and get hands-on Data Science experience is to join an open-source community. Providing solutions to projects that are already in progress will help you deal with real-world problems, while giving you a taste of what working in a Data Science team would be like.
As a member of an open-source community, you need to constantly communicate with the other stakeholders when making your contributions. Open-source projects are an excellent way to access Data Science libraries, such as NumPy, Pandas, Scikit-learn, and more. Above all else, being a part of these communities will help you build a professional network with relevant people in the Data industry, and also significantly add to your existing knowledge.
Make tutorial / educational content
If you have confidence in your Data Science skills and knowledge, you can try authoring a Data Science blog feed, or creating tutorial videos that explain the core concepts of Data Science. These are excellent ways to highlight your abilities to prospective employers.
In-person meetups
After you complete a Data Science course, in-person meetups can present great opportunities for face-to-face interactions with industry leaders and representatives. Meetups are essentially corporate events being held in your city, such as business conferences, presentations, seminars, expos or coding competitions.
These events are excellent venues for networking with like-minded professionals who work for a range of different organizations. A simple Google search with keywords like Data Science meetups, along with the name of your city, will generate information about ongoing or upcoming events near you.
Volunteer for a good cause
Many non-profit organizations need Data Science professionals to volunteer for them. This is a good way to give back to society, while at the same time, you could get to work alongside experienced Data Scientists who can guide you and offer valuable career advice.
The tasks you perform can be showcased in your resume, and will be considered as valid work experience. Poverty, Environmental Protection, Equal Education, Public Health and Human Rights are some of the non-profit areas that you can contribute to.
Conclusion
The career scope for a Data Scientist is tremendous, but it often proves difficult to get a Data Science job without a certain amount of relevant work experience. The key is to show recruiters that you possess the requisite expertise and skills to do justice to the job if you are given the opportunity, and the steps listed above will go a long way towards accomplishing that.
