Data science internships provide opportunities to learn from the pros, gain practical experience and build a strong professional network. But, getting a data science internship is not straightforward, you need to showcase your expertise and enthusiasm to work for the company. Firstly, one would require a strong foundation of data science concepts. Then the next step would be to build projects starting with the simpler ones to the ones that have real-world use cases. These add value to your overall skills and take you a step closer in your data science journey.
It takes a lot of work, from basics like creating a resume and LinkedIn profile to posting them on job boards, a lot of effort goes into it. Some of the tips which are essential to get a better data science internship are listed below. (Keep in mind, basic steps like preparing a strong resume and cover letter are something that is important too).
Solidify Knowledge
Getting an internship is similar to getting a job. No matter which one it is, one has to be proficient in the basics of data science concepts that are a must-have — statistics, programming languages like Python or R are essential for any data scientist.
One can take up online resources that can prove to be more flexible or opt for offline classes. Also, one needs to follow sites like AIM, KDNuggets, and Kaggle, to stay updated with everything that’s going on in the data science community.
Soft Skills
One of the crucial things recruiters will look at while hiring a data science intern is soft skills. These include strong written and verbal communication skills. Soft skills help interns to develop a relationship with stakeholders for the company’s betterment. Besides, it will assist you while collaborating with different departments of an organisation. Data scientists are expected to share ideas, insights, and criticism to successfully communicate their results. In a large company, one needs to have soft skills to successfully collaborate with stakeholders.
Research
One of the most advisable courses of action for your data science strategy for landing a better internship would be by making a list of companies that you would be interested in. This list can be based on the industry, culture, location and brand appeal. It’s important to know about these factors when it comes to selecting these companies. When you have successfully made a list, then its time to understand what they are looking for and how well you can contribute to that company. Once you have the list, look at the skills required for these companies. This will help you streamline your options and possibly help you choose the right internship.
Building An Attractive Portfolio
Building a strong portfolio doesn’t only involve working on projects and displaying them on your resume. Part of building a portfolio can also involve blogging or showcasing projects on platforms like Kaggle or GitHub. Besides, try to contribute on StackOverflow to gain reputations as it will communicate the expertise you have in the data science techniques. You can also participate in MachineHack to test your skill against other data science enthusiasts and learn in the process. Participation is key for aspirants than winning a hackathon. This demonstrates your enthusiasm and interest to thrive in the competitive industry.
Finding A Mentor
To thrive in a competitive landscape, you need to adopt best practices, which you can get from mentors. Good guidance from data science leaders can help you in devising a perfect strategy to obtain an internship. You can register here to get your questions answered from prominent data scientists. Mentors can not only show you the right path but also motivate you to keep going in the competitive data science space.
Internship Search
After you have taken care of the basics that involve making an attractive resume and cover letter, the next step is to put this information on a job board, particularly the ones that are related to data science. Some of the recommended job boards and places you can search for internships are: Internshala, Github, AngelList, Kaggle Jobs, Analytics India Magazine, LinkedIn.
