How to Land a Data Science Internship: Your 2024 Guide

Written by Coursera Staff • Updated on

You can gain professional experience with a data science internship. Learn more about what you can do to find one with this guide.

[Featured Image] A woman makes copies at a copier in an office setting for her data science internship.

A data science internship can be the first step to starting a career as a professional. Offering hands-on experience in the work world, internships can provide networking opportunities, give you the chance to work with complex data sets, collaborate in a team environment, and experience the direct impact of your field of study. In this article, you'll explore valuable information about data science internships—from the benefits and requirements to where to find them and what you should consider before applying. At the end, you'll also find online courses that can help you hone the critical skills you'll need to excel in the field.

Data science interns: benefits, duties, and required skills

Each data science internship will have unique benefits, duties, and skills required for the position. While every internship is different, the following sections outline what you can expect from most data science internships. 


Internships offer many benefits, including gaining work experience, practicing your technical skills in the real world, and building your resume as you start your career. 

Internships typically last a fixed amount of time, such as four months, and allow students and early career professionals to connect with a network of industry professionals. In some cases, an internship may lead directly to a job later. 


As a data science intern, the necessary skills may vary between positions. As a graduate student, you'll likely perform more duties than an undergraduate in a similar position. 

The list below includes some common skills that interns may encounter, which are expected of data scientists. 

Technical skills

Data scientists have a well-honed technical skill set that allows them to gather, analyze, and visualize data while developing data models that guide decisions and predict outcomes. 

To perform all these critical duties, data scientists must often have a firm grasp of the following technical skills: 

  • An understanding of programming languages, such as SQL, Python, or R.

  • The ability to visualize data by using such data science tools as Tableau and PowerBI.

  • An understanding of machine learning to collect data and develop predictive machine learning models.

  • A grasp of software frameworks, such as Hadoop, for processing big data. 

Workplace skills

Data scientists must work with experts and non-experts to ensure they collect the best data and make the best decisions. As a result, they must embody some of the following workplace skills: 

  • Teamwork

  • Communication 

  • Problem-solving 

  • Storytelling 

Data science internships: Where to find them, requirements, and tips on applying

The first step to landing a data science internship is finding one. In this section, you'll learn where you should look, some common requirements, and tips to help you when you apply. 

How to find a data science internship

You can find a data science internship by searching for available internships through your university, such as the Metropolitan Data Science Association (MDSA) of Toronto Metropolitan University. You can find internships on job posting websites or by searching for internships on a business’ dedicated job page. 

Job posting sites

To find a data science internship on a job posting site, go to your site of choice and search for “data science internship.” From there, you can view a list of currently available internships.

Some popular websites to search for internships include: 

Business’ dedicated job page

To find an internship on a business’ dedicated job page, go to a search engine, such as Google, and search “[business’ name] jobs.” Once on the dedicated job page, search for “data science internship” and see if any are available. You might also consider searching for a related position, such as “data analytics internship” or “data science engineer internship.” 

Data science internship requirements

The requirements for each data science internship will depend on the nature of the position. Nonetheless, some of the most common internship requirements include: 

  • A grasp of programming languages, such as Python, Java, or C++ 

  • An understanding of scripting languages, such as SQL, Python, and R 

  • An ability to work with others in a fast-paced environment 

  • Research experience 

  • Enrollment in an undergraduate or graduate degree program in computer science, mathematics, statistics, or a related field 

Keep searching

While many data science internships are geared toward those already enrolled in a relevant master’s degree program, some target undergraduates or even high schoolers. No matter your current education level, there are likely internships available.


Tips for applying

When applying for your internship, you’ll want to put your best foot forward. Consider the tips below to help improve your chances of landing your next internship.

1. Apply in advance. Internships can be competitive, so it’s helpful to make a good first impression by applying before the deadline. For example, if you're applying for a summer internship, you'll want to apply much earlier in the year. Sometimes, you may even want to apply in the fall of the previous year. Being early will also give you time to ensure all your application material is up to par. 

2. Apply to more than one internship. Data science internships can attract many strong applicants, so it’s best to apply to many internships rather than pinning your hopes on just one. 

3. Create a portfolio. You can highlight your skills by creating a portfolio of your past or current work. This can be a good addition to your application. 

4. Lean into your network. Your existing network of professional contacts could be a helpful resource for identifying available internships and soliciting feedback on your application material. 

5. Get a recommendation letter ready. Your application will likely require a letter of recommendation from a teacher or relevant individual who knows you well. Writing a recommendation letter can take some time, so ask your recommender at least three weeks before the deadline. 

6. Fine-tune your resume and cover letter. Tailor your cover letter and resume to each internship. Your cover letter and resume are your professional calling card, so proofread both before submitting them. 

Types of data science internships

As you search for data science internships, consider specializing in an area related to your interests and future career goals to get the most value from your experience. You can also use an internship to help you decide which career path to take. Some types of data science internships include:

  • Data analyst intern

  • Machine learning engineer intern

  • Data scientist intern

  • Business intelligence intern

  • Data engineering intern

  • Natural language processing (NLP) intern

  • Computer vision intern

  • Data security intern

  • Health care data science intern

  • Marketing analytics intern

Get ready for your internship.

A data science internship offers valuable hands-on experience that can enhance your career or help you decide which career path interests you most. To prepare for this next phase of your professional life, you might consider taking a relevant course online.

While the University of Michigan’s Python for Everybody Specialization offers beginners a crash course in programming, the IBM Data Science Professional Certificate can help you gain career-relevant skills and experience. 

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