Online Degrees Blog at New York Tech
Data Science Internships: Benefits and Tips for Success

Data Science Internships: Benefits and Tips for Success

Male intern working on desktop computer in an office space.

Data science internships for beginners and experienced professionals are available in a variety of industries, ranging from finance and technology to healthcare and government. Aspiring data scientists should seek out internships to gain practical experience and help build out their resume.

The good news is that many of these internships are paid, and the average data science paid internships' salary is $91,099 per year, according to Glassdoor.1 Additionally, internships open doors to career paths you might not have previously considered and make it much easier to land a full-time position in data science. About 68% of college interns receive job offers from the companies where they interned.2

This post explores why data science internships are important, how they shape your future career path, and tips for getting the most out of your internship experience.

Benefits of Data Science Internships

In today’s competitive job market, internships give you an edge when seeking full-time data science roles. To truly benefit from the experience, be sure to identify internships that match your goals, interests, and values. Expect meaningful opportunities to work alongside industry experts, apply classroom learning to real-life challenges and solve problems, and build strong professional connections. Additionally, the best internships will match you with a mentor that guides and nurtures your professional development throughout the program.3

Finding the Right Data Science Internship

Currently, there are more than 10,710 data science interns employed at U.S. companies, and there can be intense competition for these opportunities.4 While your dream job might be at a tech giant, such as Amazon, Microsoft, or Facebook, don’t overlook the many data science internships available in banking, finance, government, and health care.3

To find the ideal data science internship for you, do your homework and stay positive during your search. Start by assessing your goals, interests, and any relevant professional experiences or formal education that will distinguish you from other applicants. Determine whether you want to do a paid internship in data science, or whether an unpaid one would give you enough hands-on experience to outweigh the cost. Think about what new skills you hope to gain and the company culture that best fits your values.

Once you have a clear idea of what you hope to gain from the experience, consult job boards. But just because a company doesn't seem to offer data science internships, you should also market yourself to companies even if you don’t see something available—you never know what will change in the future, especially if the company hires a lot of data scientists and data engineers. Finally, take advantage of all resources—including your university’s alumni organization and career center—to maximize your chances of finding the right data science internship.

Application and Preparation

If this is going to be your first data science internship, then you want to make sure that you have all the materials, time, and basic skills required to apply. You can always take online courses to gain some necessary skills and a fundamental understanding of the data science field before getting started.

Tailor Your Resumes and Cover Letters

Companies seek data interns with experiences in math, statistics, data visualization, machine learning, data analytics, and programming languages ( e.g., Python, R, and SQL), as well as prior project experience. Before you start applying, create a list of data science projects, team contributions, and lessons learned from your coursework or professional experiences.5 Also highlight your soft skills, such as collaboration, organization, and good communication skills.

If you’re applying to internship posts, identify relevant keywords and incorporate them into your resume and cover letter. This allows you to tailor your application to the role for which you are applying and increases the likelihood it will pass through application tracking system (ATS) that companies, large and small, use to identify only the most qualified candidates.5

Prepare for Technical Interviews and Coding Challenges

Since so many data science roles are based on output, you may need to demonstrate your data science skills by completing a technical interview or coding challenge. Set yourself up for success by researching common interview questions and completing practice coding challenges.6

Navigating the Internship Experience

The data science internship is the time to put your math abilities, statistical knowledge, and programming skills to work. Your experience will vary based on the company’s culture, size and other factors. You will likely get to hone your skills in collecting, analyzing, and interpreting data sets to improve products and support business decisions. If you are interning at a company that values teamwork and innovation, you could coordinate with functional teams to implement data models and monitor results. At a smaller company, your internship might include getting to see a project’s implementation from start to finish.

Making the Most of the Internship

Regardless of the company culture or size, make the most of your time as a data science intern. Learn as much as you can about project goals and objectives. Ask questions to clarify your role and responsibilities and seek guidance from mentors and other team members. Be open to constructive feedback that helps you identify your strengths and areas for improvement, and try to apply those learnings during your internship and beyond.

Building Relationships and Networking

Networking is one of the most effective ways to land a data science internship for college students, and it’s a skill that will be critical to your success during the internship. Learning about others’ own data science careers can help you sharpen your own focus. So, take the time to learn from experts on your team and managers who are leading interesting projects.7 Once you have a firm grasp of your own goals, you can more effectively communicate what you want to hiring managers.

After your internship, be sure to maintain relationships with the contacts you have made. Also, consider joining data science organizations where you can continue to engage in networking and professional development. The Association of Data Scientists and the American Statistical Association are groups that host conferences, webinars, and workshops, which help you stay abreast of industry changes and gain new skills.8

Showcasing Internship Achievements

Add internship achievements to your resume and data science portfolio. If you don’t already have an active Github profile or a presence on Kaggle, join these data science communities to showcase your learning, practice new skills, and participate in competitions and small projects.9 Including these items to your resume can help you stand out during your search for an internship and full-time data science role.

Distinguish Yourself as a Data Science Leader

While a data science internship can teach you a lot about how to be a successful data scientist, or how to excel in a data science role, nothing compares to the in-depth, comprehensive education that a master's degree provides.

Invest in your future by pursuing an Online Data Science, M.S. at New York Institute of Technology. Our cutting-edge courses are designed by top faculty to prepare you for the evolving demands of the job market. You’ll develop sophisticated technical skills that will make you stand out from other internship applicants, as well as build a strong foundation in data science that will maximize your earning potential. You can further distinguish yourself by taking specialized electives in artificial intelligence, information security, and other areas.

Get in touch with an admissions outreach advisor today to learn more.

New York Institute of Technology has engaged Everspring, a leading provider of education and technology services, to support select aspects of program delivery.