How to Land a Data Science Internship: Your 2023 Guide

Written by Coursera • 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 college student sitting on a step outdoors applies to a data science internship on his laptop computer.

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. But what can you expect from a data science internship, and how do you find one? 

In this article, you’ll learn all you need to know about data science internships – from their benefits and requirements to where you can find them and what you should consider before applying. At the end, you'll also explore cost-effective, 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

If you’re seeking an internship, then you're likely curious about the benefits, duties, and skills required by the position. While every internship is different, the following sections outline what you can expect from most data science internships. 

Benefits

Internships offer many potential benefits, including the opportunity to gain work experience, practice your technical skills in the real world, and build out your resume to help you as you set out on your career path. 

Typically lasting for a fixed amount of time, such as four months, internships also allow students and early career professionals an opportunity to connect with a network of industry professionals. In some cases, an internship might even lead directly to a job later. 

Skills

As a data science intern, the skills you’ll use may vary from one position to another. If you are a graduate student, you'll likely be expected to perform many more duties than an undergraduate in a similar position. 

The list below includes some of the most common skills expected of data scientists, which you will likely encounter as an intern in the field. 

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: 

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

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

People skills

Data scientists must work with both experts and non-experts to ensure that the best data is collected and the best decisions are made. As a result, they must embody some of the following common people skills: 

  • Teamwork

  • Communication 

  • Problem-solving 

  • Storytelling 

Read more: Hard Skills vs. Soft Skills: What’s the Difference?

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Data science is the art of uncovering the insights and trends that are hiding behind data.

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 on a job posting website 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 simply 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 simply search “[business’ name] jobs.” Once on the dedicated job page, search it 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. 

  • To be enrolled in an undergraduate or graduate degree program in computer science, mathematics, statistics, or a related field. Some internships may only be targeted at those in graduate degree programs.

Keep searching

While many data science internships are geared towards 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 to you.

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Tips on applying

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

1. Apply in advance. Internships can be competitive, so it’s helpful to make a good first impression by applying well in advance of the deadline. For example, if you're applying for a summer internship, then you'll want to apply much earlier in the year or, in some cases, 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 in action by creating a portfolio of your past or current work. This can be a good supplementary 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 make sure to proofread both before submitting them. 

Read more: How to Write an Internship Cover Letter: 9 Tips (+ Examples)

The data science internships available to you will continue to change as each position is filled and new ones become available. Yet, according to trending search results, some data science internships are particularly popular in 2023, such as:

  • Amazon data science internship 

  • Microsoft data science internship 

  • Nike data science internship 

  • Meta data science internship

  • Google data science internship

  • Coursera internship

As you are searching for your internship, consider searching for the above internships to help you get a sense of what some other candidates are looking for – you might even land one yourself. 

Get ready for your internship.

A data science internship can mark the beginning of a new career. 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, IBM’s Data Science Professional Certificate equips course takers with career-relevant skills and experience. 

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professional certificate

IBM Data Science

Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required.

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Written by Coursera • Updated on

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