Data science in the USA is highly competitive and offers many opportunities for student interns. Companies looking to hire students for internship programs generally expect them to have strong knowledge of statistical data dissemination, Python, and machine learning. Students are also expected to apply theoretical knowledge to real-world business scenarios. Hence, students who interview for internships, while not necessarily required to have a strong MLOps background, should still have some familiarity with model evaluation, basic deployment concepts, and cloud fundamentals.
During interviews, an interviewer will gauge students’ ability to explain technical information in layman’s terms, think through open-ended case study scenarios, describing their project experience in a hands-on environment. This blog shares data science interview questions for US students to help you prepare as effectively as possible.
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Common Data Science Internship Interview Questions for USA Freshers
In the USA, data science internships typically assess your foundational knowledge of data science, practical projects, and problem-solving skills. Interviewers will evaluate your knowledge of key data science concepts to determine your ability to use them when solving actual business issues, regardless of experience level.
Some general questions for Data Science internship interviews are:
- Describe yourself and why you are interested in pursuing a career in Data Science?
- Why do you want to intern with us?
- What are your expectations to achieve from this internship?
- What tools do you like to use when analyzing data?
- Do you have any experience with cloud platforms (AWS/GCP/Azure)?
Technical Interview Questions for Data Science Internships
When it comes to getting an internship in Data Science through a technical interview, you will be evaluated on your comprehension of the foundational principles of Data Science, coding capabilities, and ability to solve real-world challenges within your field. If you are a newcomer, you will be expected to have strong knowledge of statistics, proficiency in Python, knowledge of machine learning, data handling capabilities, and a strong ability to verbalise what you have done and how you got to the solutions.
Below are some examples of technical questions for Data Science internship interviews:
- What is the difference between supervised and unsupervised learning?
- What is the process to handle missing data in a dataset?
- What is cross-validation? Why is it used?
- What is overfitting? How to prevent it?
- What is the bias-variance tradeoff?
Behavioral & HR Interview Questions for Freshers
In behavioral and HR interview rounds, the interviewer will evaluate your ability to communicate effectively, work well in a team, adapt to change, and be a good cultural fit for the company. Interns are assessed on their learning attitude, problem-solving skills, and ability to face real-world challenges.
- Describe a project that you worked on, which was challenging, and how you handled it
- How do you prioritize task completion, especially when working on multiple crucial projects?
- What do you do to stay relevant with the latest data science developments?
- Can you handle criticism or negative feedback on your work? How?
- How would you explain your model to a non-technical stakeholder?
Tips to Prepare for a Data Science Internship Interview in the USA
When approaching a data science internship interview in the US, prospective candidates should have a thorough understanding of both the technical aspects and each company’s revenue-producing avenues. The following are ways in which a prospective candidate can differentiate themselves from others in a data science internship interview questions and answers:
- Revisit position requirements or the skill set required for the internship: If the position requires Python experience, align it with previous academic projects from your undergraduate studies. Be prepared to support your project experience with facts from the projects you have completed in university, culminating in a final capstone project.
- Research the services/products/data usages of the targeted companies: Try to find how the company you are applying to utilizes data sciences for things like customer behavior analysis, recommendation systems to assist consumers about the choices available to them, fraud detection in finance, or how you would use data science in their day-to-day operations.
- Know their competition and how they use artificial intelligence and analytic tools: Identify examples of companies in your industry that have embraced new technology and used it to their advantage. Also, for companies based in the US, understanding trends in generative artificial intelligence, predictive analytics, and cloud-based machine learning products will help provide the company with valuable insights during interview discussions.
Also Read: Essential Data Science Skills Taught in Online Courses for US Students
How upGrad Helps You Prepare for Data Science Careers in the USA
There is significant growth potential in data science, driven by rapid technological and data advances and by job opportunities across multiple industries. upGrad facilitates aspiring data scientists through highly relevant, industry-aligned online courses and helps them meet the evolving demands of the US job market. This includes short-term boot camps and professional certificates, as well as a master’s degree from multiple global universities. Students learn in a hands-on, project-based environment, building their skills to match the demands set by employers.
Here are some relevant programs to explore:
- Executive Post Graduate Certificate Programme in Data Science & AI from IIIT-B
- Master of Science in Data Science from Liverpool John Moores University
- Executive Diploma in Data Science and AI from IIIT-B
Related Articles
- How to Build a Data Science Portfolio to Secure Your First Job in the US
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FAQs on Data Science Internship Interview Questions for Freshers
Some of the common data science internship questions include:
Tell me about yourself and why you are interested in data science.
Why do you want to intern with our company?
How would you handle missing data in a dataset?
What is the difference between supervised and unsupervised learning?
Interns may need some understanding of Python, including proficiency with data manipulation libraries like Pandas and NumPy, data visualization tools like Matplotlib and Seaborn, and basic machine learning libraries to secure a data science internship.
Yes, taking on projects is important when applying for internships. Since most interns don’t have any experience, projects are a way to showcase their skills and data science abilities.
Here are some common SQL questions asked in Data Science internships interviews:
What are the key differences between SQL and NoSQL databases?
Name the primary SQL data types?
What is a subquery?
How to prevent duplicate records when making a query?
Yes, international students can apply for data science internships in the US, primarily using Curricular Practical Training (CPT) (during their studies) or Optional Practical Training (OPT) (during post-graduation) through their F-1 visa. These opportunities allow students to gain industry experience.
