How to Get a Data Science Internship in 2024

Despite being a growing industry with a $349 billion market volume finding a decent data science internship is becoming consistently challenging due to the competitiveness of the industry.

Academic leniency toward data modeling, often neglecting essential phases like ETL and deployment in real-world projects, also frequently limits internship opportunities.

Regardless, with a proper preparation strategy and guidance from this article, it’s possible to land a data science internship seamlessly. We’ll cover how to land a data science internship and share a few tips to effectively implement the strategies, but first, let’s discuss what should you expect as a data science intern.

Internships rarely differ from full-time employment in terms of responsibilities and skills. While there may be a disparity in data scientist salaries, it’s often offset by the exposure to projects and industry practices. Here is what you should expect as a data science intern:

Real-World Hands-On Experience

Access to real-world data and its imperfections might help you gather the much-needed practical experience you may need to function as a full-time data scientist at other companies. You’ll also be working alongside experienced data scientists—studying their approach and methodologies.

An internship could be a great opportunity for you to be involved in tasks like data cleaning, analysis, and model building without stressing too much about performance and appropriation. Depending on the company, you might also contribute to different stages of projects from start to finish, gathering real-world hands-on experience.

For instance, you could be tasked with creating an efficient data science workflow in the company. This involves pinpointing areas for enhancement, importing relevant data, constructing and validating a model, and finally deploying it into operation.

Learning and Developing New Skills

You’re likely to be exposed to new tools, technologies, and methodologies during your internship. It’s a great way to learn new skills and improve existing ones. For example, interns often delve into exploratory data analysis (EDA) to understand the underlying structures and patterns within datasets with real-world implications, that are often overlooked in academic exercises.

During your tenure as an intern, you’re likely to be introduced to machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning. Regression, classification, and clustering techniques will possibly be involved in these projects.

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If you happen to land an internship with a user-centric company, big data technologies, such as Hadoop and Spark may also be involved in the process.

Creating Data Visualizations

Visualizations are most effective when they tell a clear story. You’ll need to be able to explain the insights conveyed by the data and how they relate to the project’s goals.

It is a critical part of data science workflow, and you’ll likely encounter it frequently during your career while conveying information to both technical and non-technical stakeholders. Data science internships present you with the opportunity to learn about different types of visualizations, their relevance, and the process of creating them efficiently.

For instance, both bar charts and line charts are effective in conveying trends over time, but bar charts are more convenient when comparing categories, and line charts are typically used when working with continuous data.

Refining Communicational Skills

Data scientists are often required to collaborate with different teams and convey complex information to a diverse audience. This calls for tailoring your communication style according to the domain expertise of the stakeholder. As an intern, you’ll face situations where you’ll be required to adapt to the level of technical details of your audience and be compelled to select appropriate terminology for them.

You’re also likely to learn active listening, written communication, and cross-functional collaboration skills during your internship.

Professional Feedback and Mentorship

You’ll benefit from the guidance and support of experienced professionals who serve as mentors. While only 37% of interns find the right mentor, they often provide valuable insights, advice, and resources to help navigate your projects and overcome challenges. They share their knowledge and expertise, offering practical solutions to real-world problems encountered during the internship.

Your mentor may also collaborate with you on projects, offering opportunities for hands-on learning through assessing your progress, identifying areas of improvement, and refining your skills. Furthermore, mentors actively act as guidance counselors to help you navigate the career paths in data science.

With more than 37% of job seekers relying on referrals and their network to enhance their careers, having a suitable mentor offers an additional advantage during the internship. With a suitable mentor, your chance of landing full-time employment within the company grows significantly.

The majority of rejected data science internship applications typically stem from candidates lacking essential skills, clear objectives, or encountering challenges with verbal and written communication. Development in these areas often raises the probability of your application being shortlisted and selected for the data science internship. Here is a step-by-step guide on how to do it:

Sharpen Your Technical Skills

Landing a data science internship requires mastering commonly used languages, such as Python, R, SQL, and potentially others like Java or Scala for big data processing. Similarly, gain proficiency in libraries like Pandas, NumPy, and SciPy for data manipulation and analysis tasks. Furthermore, get a deep understanding of machine learning algorithms, techniques, and frameworks such as scikit-learn, TensorFlow, or PyTorch.

Develop Soft Skills

As mentioned, soft skills are the key when it comes to being successful as a data science intern.

Sharpen your communication skills to effectively convey complex technical concepts to non-technical stakeholders through presentations, reports, and visualizations. Develop strong teamwork and collaboration skills by contributing to group projects, hackathons, or data science GitHub repositories.

Additionally, practice problem-solving skills through challenges, takehome assignments, and real-world dataset projects to demonstrate your ability to tackle complex data science problems.

Build Your Portfolio

Build a strong portfolio to bolster your candidacy over other prospective interns. Prepare for the internship interview by working on a variety of data science projects to showcase your skills and expertise. Include project details, methodologies, and outcomes in your portfolio to demonstrate your ability to apply data science techniques to real-world problems.

If possible, create a GitHub repository to host your projects and share your approach with potential employers. Regularly update your repository with new projects and contributions to demonstrate your commitment.

RECOMMENDED: Top 15 Python Projects for Your Resume to Make It Better

Leverage Online Presence

Optimize your LinkedIn profile to highlight your data science skills, projects, and experiences. Connect with professionals in the field and join relevant data science groups or communities to expand your network. Personal websites and blogs also work pretty well to showcase your projects, insights, and expertise in data science.

Craft a Strong Application

Tailor your resume according to the opportunity to highlight relevant skills, experiences, and projects related to data science. Quantify your achievements and use action verbs to describe your contributions effectively.

Learn to write a compelling cover letter that explains why you’re interested in the internship, what you can bring to the role, and how your skills align with the company’s needs and values. Ensure that your application materials are error-free, well-organized, and visually appealing to make a positive impression on recruiters and hiring managers.

It’s advisable to take professional help while tailoring the CV and crafting the cover letter.

Practice Interview Questions

Practice answering data science interview questions related to programming, data manipulation, statistics, machine learning, and data visualization.

Also, prepare responses to behavioral interview questions that demonstrate your problem-solving abilities, teamwork skills, and adaptability in various situations.

Participate in Mock Interviews

Take advantage of our Mock Interview Portal where you share the platform with other interview candidates and get constructive feedback on your responses in the simulated environment.

Here are some unconventional tips to land your data science internship:

Create interactive Data Science Projects

Instead of relying on static project results, create interactive projects that allow recruiters to engage with your work. Building a dashboard, for instance, will allow the hiring manager to assess your application not only on the merit of technical skills but also on creativity and communication skills.

Contribute to Data Repositories

Explore beyond traditional datasets and contribute to unconventional data repositories or projects. For example, collaborating with researchers in niche fields and contributing to open-source projects that focus on unique problems may accentuate your portfolio to recruiters seeking innovative thinkers.

Showcase Your Data Storytelling Skills

Data storytelling goes beyond traditional charts and numbers to craft compelling narratives that resonate with the audience. Experiment with formats such as data comics, interactive narratives, and multimedia presentations in your portfolio to demonstrate your ability to communicate complex ideas in engaging ways.

Create a Portfolio Newsletter

If you have substantial followers, share interesting datasets, analysis techniques, and industry trends with subscribers to showcase your expertise and thought leadership. This unconventional approach not only demonstrates your skills but also helps you build a personal brand and network within the data science community.

Participate in Lesser-Known Competitions

Participate in lesser-known data science challenges and competitions outside of popular platforms. Explore niche competitions hosted by organizations, universities, or research institutions that focus on specific domains or problem areas. Making significant contributions or winning these challenges can attract attention from recruiters

Target Smaller, Less Competitive Companies

Consider applying for internships at smaller companies or startups. They might have fewer applicants and be more open to taking a chance on a promising intern with strong potential. These internships can often provide a more hands-on experience and allow you to contribute to impactful projects within a growing company.

Can you land a data science internship if you have zero experience?

Yes. It is possible to land a data science internship with no prior experience, but it requires extra effort to showcase your potential. This may include building a strong skills foundation, highlighting transferable skills, creating a portfolio, and networking.

What are the most popular companies that offer data science internships?

Among the booming companies that offer data science internships, Google, Meta, Amazon, Apple, Microsoft, Accenture, Deloitte, Pfizer, Walmart, and McKinsey & Company are noteworthy.

Check out our Company Interview Guides section if you have a specific company that you want to apply for.

Do you need experience to become a data science intern?

While some internships might have requirements like specific coursework or programming experience, it’s not always mandatory. A strong foundation in core data science skills, eagerness to learn, and a willingness to take initiative can compensate for a lack of direct experience.

How do you write a resume for a data science internship?

Writing an effective resume for a data science internship entails tailoring your CV, quantifying your achievements, including relevant keywords and technical skills, and describing your project experiences.

What skills are most relevant for data science interns?

Python, SQL, statistics, and machine learning fundamentals are the most relevant skills for data science interns. In addition, communication, teamwork, critical thinking, and problem-solving skills are considered paramount.

Can a data science internship lead to a full-time job?

Absolutely! Data science internships are a fantastic way to gain hands-on experience, build your network, and impress potential employers.  Performing well during your internship can significantly increase your chances of landing a full-time job offer after graduation.

Hopefully, you’ll be able to refine your technical skills, develop soft skills, build a portfolio, and submit a strong application for the internship you are eyeing for.

If you’re looking for places where you can participate in mock interviews or practice interview questions, then look no further and check out our features here on Interview Query.

Read through our blog if you want to explore more of our content, or visit our subreddit if you have any concerns.

We wish you all the best for your upcoming data science internship interview. We look forward to hearing your success story.