How to Land a Data Science Internship: Your 2025 Guide

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 you can 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.

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Python for Everybody

Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data.

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Database Systems, Database Design, Application Programming Interface (API), Extensible Markup Language (XML), Data Collection, Data Visualization, SQL, Python Programming, Relational Databases, Network Protocols, Web Services, JSON, Data Structures, Restful API, Data Modeling, Data Processing, Programming Principles, Web Scraping, Database Management, Computer Programming, File Management, Development Environment, Data Manipulation, Data Import/Export, Data Visualization Software, Data Analysis, Exploratory Data Analysis, Unstructured Data, Algorithms, Object Oriented Programming (OOP), Software Installation, Hypertext Markup Language (HTML), TCP/IP, Data Capture, Data Access

Data science internship: 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. 

Benefits

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

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Fractal Data Science

Launch your career in data science. Build job-ready skills and hands-on experience for an in demand career in as little as 5 months. No degree or prior experience required.

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Human Centered Design, Database Design, Data Storytelling, Data Visualization, Power BI, Data Analysis Expressions (DAX), Decision Making, SQL, Feature Engineering, Machine Learning, Data Presentation, Python Programming, Storyboarding, Applied Machine Learning, Exploratory Data Analysis, Data Modeling, Problem Solving, Data Manipulation, Interactive Data Visualization, Critical Thinking, Empathy, Data Analysis, Complex Problem Solving, Analytical Skills, Critical Thinking and Problem Solving, Data Science, Data Visualization Software, Business Intelligence, Data Cleansing, Data Transformation, Microsoft Excel, Business Reporting, Dashboard, Key Performance Indicators (KPIs), Machine Learning Algorithms, Regression Analysis, Random Forest Algorithm, Performance Tuning, Artificial Intelligence and Machine Learning (AI/ML), Classification And Regression Tree (CART), Supervised Learning, Decision Tree Learning, Algorithms, Predictive Modeling, Self-Awareness, Behavioral Economics, Communication, Psychology, Probability, Emotional Intelligence, Artificial Intelligence, Unsupervised Learning, Statistical Modeling, Anomaly Detection, Data Processing, Application Deployment, Infographics, Storytelling, Data Literacy, Data Ethics, Drive Engagement, Data-Driven Decision-Making, Probability & Statistics, Statistical Analysis, Jupyter, Statistics, Descriptive Statistics, Data Wrangling, Pandas (Python Package), Query Languages, MySQL Workbench, Relational Databases, Database Management, Data Integrity, Databases

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: 

• 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 Power BI.

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

• A grasp of software frameworks, such as Apache 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, you must embody some of the following common people skills: 

• Teamwork

• Communication 

• Problem-solving 

• Storytelling 

Data science internships: Where to find them, entry 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’s 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: 

• Handshake

• LinkedIn

• Indeed

• Vault

Business’s dedicated job page

To find an internship on a business’s dedicated job page, go to a search engine, such as Google, and search “[business’s 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.” 

How to prepare for a 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, internships are likely available to you.

How to get a data science internship: Tips on applying

When applying for your internship, 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, 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 materials are complete. 

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. A portfolio of your past or current work can highlight your skills in action and be a good supplement 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 represent you professionally, so make sure to 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 data science internship with Coursera

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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Python for Everybody

Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data.

4.8

(216,394 ratings)

1,813,449 already enrolled

Beginner level

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Average time: 2 month(s)

Learn at your own pace

Skills you'll build:

Database Systems, Database Design, Application Programming Interface (API), Extensible Markup Language (XML), Data Collection, Data Visualization, SQL, Python Programming, Relational Databases, Network Protocols, Web Services, JSON, Data Structures, Restful API, Data Modeling, Data Processing, Programming Principles, Web Scraping, Database Management, Computer Programming, File Management, Development Environment, Data Manipulation, Data Import/Export, Data Visualization Software, Data Analysis, Exploratory Data Analysis, Unstructured Data, Algorithms, Object Oriented Programming (OOP), Software Installation, Hypertext Markup Language (HTML), TCP/IP, Data Capture, Data Access

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IBM Data Science

Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.

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Skills you'll build:

Jupyter, Generative AI, Data Wrangling, Matplotlib, Unsupervised Learning, Plotly, Data Visualization, Predictive Modeling, SQL, Feature Engineering, Data Analysis, Data Visualization Software, Supervised Learning, Data Mining, Exploratory Data Analysis, Dashboard, Professional Networking, Pandas (Python Package), Interactive Data Visualization, Data Literacy, Regression Analysis, Descriptive Statistics, Data Manipulation, Scikit Learn (Machine Learning Library), Statistical Modeling, Data Pipelines, NumPy, Data Cleansing, Data Import/Export, Python Programming, Data-Driven Decision-Making, Data Synthesis, Predictive Analytics, Data Presentation, Data Science, Natural Language Processing, Data Ethics, Data Storytelling, Data Modeling, R Programming, GitHub, Git (Version Control System), Machine Learning, Big Data, Other Programming Languages, Query Languages, Statistical Programming, Cloud Computing, Application Programming Interface (API), Version Control, Classification And Regression Tree (CART), Dimensionality Reduction, Decision Tree Learning, Applied Machine Learning, Relational Databases, Databases, Stored Procedure, Database Management, Database Design, Transaction Processing, Interviewing Skills, Applicant Tracking Systems, Portfolio Management, Professional Development, Recruitment, Presentations, Communication, Talent Sourcing, Business Research, Writing, Job Analysis, Problem Solving, Company, Product, and Service Knowledge, Deep Learning, Digital Transformation, Artificial Intelligence, Histogram, Scatter Plots, Seaborn, Box Plots, Geospatial Information and Technology, Heat Maps, Data Structures, Object Oriented Programming (OOP), File Management, Web Scraping, Programming Principles, Restful API, Computer Programming, Data Processing, Data Collection, Data Quality, Business Analysis, Analytical Skills, User Feedback, Stakeholder Engagement, Peer Review, Machine Learning Methods

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