A data science internship can be the first step to a career as a data science professional. But why do they matter, 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.
If you’re seeking an internship, you might be curious about the benefits, duties, and skills required. While every internship is different, the following sections outline what you can expect from a data science internship.
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.
As a data science intern, the skills you’ll use may vary from position to position. If you are a graduate student, for example, you will 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 a data science intern.
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 do this, data scientists must often have a firm grasp of the following technical skills:
An understanding of programming languages, such as Python, R, and SQL.
The ability to visualize data by using such data science tools as Tableau and PowerBI.
An understanding of how to use machine learning to collect data and develop predictive models.
A grasp of software frameworks, such as Hadoop, for processing big data.
Data scientists must work with both experts and non-experts to ensure that the best data is collected and the best decisions are made. To do this, they must embody some of the following common people skills:
The first step to landing a data science internship is finding one. In this section, you will learn where you should look, some common requirements, and tips to help you when you apply.
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.
To find a data science internship on a job posting site, go to your job posting of choice and simply search for “data science internship.” From there you can then view a list of currently available internships.
Some popular websites to search for internships include:
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.”
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.
To be enrolled in an undergraduate or graduate degree program in computer science, math, statistics, or a related field. Some internships may only be targeted at those in graduate degree programs.
Note: 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.
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. 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.
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 2022, including:
Amazon data science internship
Nike data science internship
IBM data science internship
Yelp data science internship
Google data science 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.
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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