Data science is a rewarding and in-demand career in the 21st century. Data scientists use their skills in programming, statistics, machine learning, and data visualization to extract insights from large and complex data sets and solve real-world problems. The employment rate of data scientists is expected to grow 36% by the end of 2031 which is much faster than the average for all occupations, stated by the Labor Statistics Bureau in the United States.
If you are interested in pursuing a career in data science, getting an internship is a great way to gain valuable experience, learn new skills, build your network, and enhance your resume. An internship can also help you explore different domains and industries where data science is applied, such as healthcare, finance, e-commerce, social media, etc,. Internships often can lead you to a full-time job offer, that is after successful completion of the internship period and your graduation.
But the question is how can you secure a data science internship in 2023? What skills do you need? How do you prepare the resume a.k.a CV and the cover letter? How are you planning to showcase the portfolio and present yourself as a personal brand? How do you ace the interview and follow up? The blog post for today is how to get internships and we will be answering some questions and sharing the tips that you can use to get internships for data science in 2023.
Where to Find Data Science Internships
The first step to securing an internship is finding the right opportunities that match your interests, skills, and goals. Many sources are available online which you can easily find where you can get data science internships, such as:
- Online job boards: There are many websites where companies post their internship openings for data science or related fields. LinkedIn, Indeed, and Glassdoor are some popular online job boards. Untapped, KDNuggets, DataJobs, and iCrunchData are data science specific job boards. You can also check out the websites of large companies like Meta, Google, Amazon, etc., that offer data science internship programs. However, remember that these internships have a high competition level. Thus they require you to have a strong grip on everything as well as a strong resume application.
- University boards: If you are a student, don’t forget to check your institution’s job boards for data science internships. Oftentimes companies are associated with university job boards so they open opportunities exclusively through the university portal. University boards are helpful in giving you an edge over applicants that do not follow these conventional methods that are not aware of such openings and opportunities.
- Startups: Another option is to reach out to startups that are working on data science or related projects. Startups are usually more flexible and open to hiring interns who can bring fresh perspectives and ideas. Internship in data science can expose you to a lot of skills and learning, that of course comes with challenging tasks. Follow and learn from mentors and professionals who are experts in the data science field. Here are things Data science masters should know.
- Data science conferences: Attending data science events is a great way to network with professionals and experts in the field. You can learn about the latest trends and developments in data science, as well as meet potential employers who may be looking for interns.
Skills Required for Data Science Internships
To secure a data science internship, you need to demonstrate that you have the relevant skills and knowledge to perform the tasks and responsibilities of a data scientist. Depending on the level and type of internship, the skills required may vary. However, some of the common skills that most data science interns need are:
- Technical skills: Strong technical skills of programming languages for data science like Python or R in addition to SQL and tools for data science like Tableau and PowerBI. On top of that, you should also know ML techniques like regression, classification, clustering, and more. The software frameworks for supporting ML development and processing big data are also important like Hadoop or Spark.
- People skills: Data scientists also need to have good communication and collaboration skills to work with different stakeholders such as clients, managers, engineers, analysts, etc., and explain their findings and recommendations clearly and effectively. They also need to have critical thinking and problem-solving skills to analyze complex data sets and come up with creative solutions.
- Domain knowledge: Data scientists need to have some understanding of the domain or industry where they are applying their skills, such as healthcare, finance, e-commerce, etc.
Related read: What is NLP in Data Science?
Writing Resume & Cover Letter
Both, Resume and the cover letter are like the first impressions a candidate can make on the recruiter. Therefore, you need to make sure that they are well-written, concise, and tailored to the specific internship that you are applying for. If you want to get some tips on building a resume and cover letter specifically for getting a data science internship then here is a list you should follow.
- Resume: Resume is to highlight the skills and education, in combination with projects and achievements that a candidate has made in the field of data science. You should use bullet points and action verbs to describe your accomplishments and quantify your impact whenever possible. Using keywords matching with the job description and list the skills and experience required for the job. Ideal length for Resume or CV is one page, but if you have extensive experience it goes over a page. Having a clear and professional layout is important as well.
- Cover letter: Your cover letter should complement your resume and explain why you are interested in the data science internship and why you are a good fit for the role. You should showcase your passion and motivation for data science, as well as your knowledge of the company and its goals. You should also highlight your skills and projects that demonstrate your ability to perform the tasks and responsibilities of a data science intern. The cover letter is not supposed to be longer than a page, it should be easy to read with a formal tone.
Building Data Science Portfolio to Secure Internship
A collection of projects showcasing the skills and abilities a candidate has in a tangible way is called a portfolio. A portfolio that shows data science skills is niche specific to data science. It can help you stand out from other applicants who may have similar resumes and cover letters, but no proof of their work. A portfolio can also help you demonstrate your creativity and curiosity for data science, as well as your domain knowledge and interests.
Following is a short list of tips for creating a personalized data science portfolio for an internship:
- Choosing projects: Choosing projects is an important part, so select the ones that are relevant to data science and align with your personal interests and goals. You can use projects that you have done as part of your coursework, online courses, personal projects, hackathons, etc. You can also look for open-source or real-world data sets that you can analyze and present in an interesting way.
- Document your projects: You should document your projects in a clear and structured way, using tools such as Jupyter notebooks or GitHub pages. You should explain the problem statement, the data source and quality, the methodology, the results, and the conclusions of each project. You should also include visualizations such as charts, graphs, maps, etc., to make your projects more appealing and understandable.
- Share your portfolio: You should share your portfolio with potential employers by including a link to it in your resume, cover letter, LinkedIn profile, etc.
- Update the portfolio regularly as you practice and build new projects or even do improvements to existing ones.
Personal Branding for a Data Science Internship
Personal branding is the way of presenting yourself to the interviewer and the world, for this part it is showing you are a data science professional. It reflects your personality, values, goals, skills, and achievements. Building a personal brand can help you establish your credibility and authority in the field of data science, as well as attract more opportunities and connections. Following is a list of some ways you can build yourself as personal brand and get data science internship:
- Create an online presence: You should create an online presence that showcases your work and expertise in data science. You can use platforms such as LinkedIn, Medium, Twitter, YouTube, etc., to share your projects, articles, videos, podcasts, etc., related to data science. You can also join online communities such as Reddit, Quora, Stack Overflow, Kaggle, etc., where you can ask questions, answer queries, participate in discussions, etc., related to data science.
- Network with others: You should network with other data science professionals and experts who can offer you advice, feedback, referrals, etc., for your data science internship. You can network with others by attending data science events such as conferences, meetups, workshops, etc., where you can meet new people and exchange ideas. You can also network with others by reaching out to them via email or social media platforms such as LinkedIn or Twitter.
- Showcase your value: You should showcase your value as a data science intern by highlighting how you can contribute to the company’s goals and objectives. You should research the company’s mission, vision, values, products, services services
You should also read: How to Become a Data Scientist Without a Degree?
How to Ace the Interview and Follow Up
The final step to getting a data science internship is acing the interview and following up with the employer. Interviews give an opportunity to showcase the skills you have gained throughout the years and show them to the interviewer. Before going to the interview prepare for it, go through the questions that can be asked, with AI tools the search for potential questions have eased so much. On top of that, the AI tools can tell you the answers to those questions as well. If you do not want to use the AI then simple Google search will do the trick. Practice technical skills and problem solving abilities, as they are a must part of the interview process.