Top Data Analyst Internship Interview Questions

As the field of data analytics continues to evolve, securing an entry-level data analyst position has become increasingly competitive. For many, internship experience no longer stands as an advantage but simply as another requirement.

However, even internship-level positions are increasingly difficult to acquire. Just as with full-time roles, prospective data analysts must navigate rigorous interview processes to secure these opportunities.

In this article, we will delve into data analyst internship interview questions often faced by candidates. Get ready to expand your knowledge on the usual topics covered, expectations from employers, and real-life interview questions.

Before diving into specific topics, it’s worth noting that data analyst internship interview questions are designed to gauge both technical acumen and the capacity to apply this knowledge in real-world scenarios.

An intern’s role is not just about crunching numbers but also understanding the narrative behind them and effectively communicating findings. Keeping this in mind, interviewers assess a blend of hard and soft skills to ensure that the candidate is not only technically proficient but also fits the organizational culture.

Here are some of the core areas you might expect questions from during a data analyst internship interview:

Structured Query Language (SQL)

When working with medium to large businesses, it is common to encounter database engines which handle the storing and querying of data, in contrast to Excel. Most analytics sources (data warehouses, for example) store relational data. To query relational data, SQL is the standard.

Knowing advanced SQL isn’t necessary for an internship role. As long as you have a strong command over CRUD (Create, Read, Update, and Delete), you should be okay to go.

Statistics and Probability

Statistics forms the backbone of data analytics. It provides the mathematical foundation to quantify patterns, correlations, and variations in the data. Here, interviewers are likely to test your understanding of key statistical concepts that are vital for hypothesis testing, data distribution assessment, and predictive modeling.

At the internship level, interviewers will often seek a grasp over foundational topics. Concepts such as mean, median, mode, standard deviation, probability distributions (normal, binomial, etc.), and hypothesis testing could be explored. However, understanding the applicability and implications of these concepts in real-world scenarios will give you an edge.

Python

The programming language of choice for many data analysts and data scientists, Python has robust libraries like Pandas and NumPy that support data manipulation and others like Scikit-learn for machine learning.

Interviewers typically look for a foundational understanding of Python syntax, along with some familiarity with commonly-used libraries. For internships, a deep dive into advanced topics may not be expected, but you should be comfortable with data structures, basic algorithms, and data-wrangling techniques using Pandas.

Data Visualization

Visualization is the art and science of turning raw data into insightful charts, plots, and dashboards. Data visualization not only helps in understanding complex data patterns quickly but also aids in communicating findings to non-technical stakeholders.

For intern candidates, proficiency in tools like Excel, Matplotlib, Seaborn in Python, or libraries in R might be sought. Familiarity with tools like Tableau or PowerBI can be an added advantage. The key is understanding when to use which type of visualization and ensuring clarity and accuracy in representation.

Behavioral

Beyond the technical know-how, interviewers also look for cultural fit, communication skills, teamwork, and problem-solving aptitude. The behavioral segment typically covers scenarios or experiences where you display key soft skills.

Preparation for this section should involve reflecting on past team projects, leadership experiences, challenges faced, and learning from failures. Using the STAR (Situation, Task, Action, Result) technique can help structure your answers effectively.

Case Study

These are scenario-based questions or tasks that aim to assess how you apply your technical and analytical knowledge in a real-world context. They also gauge your problem-solving ability and approach to breaking down complex problems.

While approaching case studies, ensure you understand the problem, ask clarifying questions if needed, break down the tasks, apply your analytical knowledge, and communicate your findings clearly. Sometimes, the process and approach are more important than the final answer.

Internship interviews and full-time job interviews might seem similar on the surface, but they serve different purposes and often emphasize different skills. While both are gateways to joining a company, the nuances in their evaluation criteria and the expectations they carry are distinct.

Objective vs. Subjective Evaluation

1. Objective Evaluation: Interviews, regardless of whether they are for internships or full-time positions, generally aim to objectively assess an applicant’s capability for the specific role they are applying for. For instance, technical interviews are designed to evaluate an individual’s knowledge and expertise in a particular field. As a result, many candidates, especially those applying for internships, prioritize mastering technical skills to excel in these interviews.

The key difference, however, hinges on the technical depth. While full-time positions have interviews on rather advanced concepts such as intermediate to difficult SQL questions and some basic machine learning knowledge, internship roles tend to focus on Excel and basic querying competency.

2. Subjective Evaluation: On the other hand, soft skills like communication, teamwork, and adaptability are equally crucial in the workplace. They might not always be the central focus of an interview, but their importance cannot be overstated.

These skills, which are more subjective, are essential for tasks such as presenting information, reporting issues, and collaborating with teams. Due to the challenge of evaluating these skills in a limited time frame, many companies incorporate behavioral interviews into their process to get a better sense of a candidate’s interpersonal and communication skills.

For most internship roles, behavioral interviews tend to focus on personal projects and team experience (such as in university). However, in full-time positions, behavioral interviews can ask about past job experiences, problems encountered, and the steps to resolve such problems.

Understanding the balance between objective and subjective skills, and how they’re evaluated differently in internship and full-time interviews, can provide a more holistic perspective on the recruitment process.

Behavioral questions in internships might seem relatively simple in practice, but in practice, it takes a data analyst with real work experience, high emotional intelligence, and background knowledge of the company in question to answer correctly.

Despite behavioral interview questions being phrased as questions with “no wrong answers,” there are a lot of wrong things that you can say during a behavioral interview. To make clear, concise, and effective answers to these types of questions, you might respond with the following structure:

• Situation - In a previous hospitality job, we noticed our conversion rate was decreasing.
• Task - My goal was to understand our customers better to help the sales and marketing team develop more effective UX strategies and promotions to improve conversion rates.
• Action - In the past, many of the marketing decisions had been made based on our average customer. But I felt that there was an overreliance on the average, and I decided to cluster customers and better segment them.
• Result - We found that our real customers weren’t very similar to the average metrics that were being used. The customer segments I created helped to better guide product and UX decisions, resulting in a 30% lift in conversion rate.

This is a general outline. You’ll want to add more depth, but it gives you an idea of how to use the STAR framework to structure your answers.

Here are some behavioral interview questions that you should review:

1. Do you work well under pressure? Do you work well on teams?

This is a classic culture fit behavioral question. Interviewers ask it to see how well you take direction, how you collaborate, and how you might fit in with the team.

As an intern, the best examples for these might be for university group activities. Express how you managed to get outputs despite deadlines and time constraints. If you do not have prior group activity experience, an optimistic outlook on team-oriented activities is a good way to answer such a question.

2. Describe a data project you worked on. What were some of the challenges you faced?

As an intern, recruiters are expecting zero to no previous professional experience from you. One way for them to gauge your data analytics skills is through your previous data analytics projects.

When you’re asked about a project, use a format like the STAR method. You should walk the interviewer through the project, from start to finish. Start with what inspired you to do the project and its conceptions. Describe your approach and the challenges you encountered during the process. Describe your results and always end with how you improved yourself upon finishing said projects.

3. How comfortable are you with presenting insights?

Interviewers want to know you’re confident in your communication skills and can effectively communicate complex ideas. With a question like this, walk the interviewer through your process:

• How you prepare data presentations.
• Strategies you use to make data accessible.
• What tools you use in presentations.

Also, the ability to present virtually is increasingly important in today’s market. Have several recent experiences to talk about, both in-person and virtual. This is a common question in data visualization interviews.

4. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

A question like this explores how you handle adversity and adapt in the moment. Be honest about what went wrong. Then, describe how you apply what you learned to future tasks.

For example, you might say:

“I presented a data analytics project to non-technical stakeholders, but my presentation was far too technical. I realized that the audience wasn’t following the technical aspects, so I stopped and asked for questions. I spent time clarifying the technical details until there were no questions left. One thing I learned was that it’s important to tailor presentations to the audience, so before I start a presentation, I always consider the audience.”

5. Tell me about a time when you had to use data to make a decision.

This is an open-ended question that interviewers use to a) understand your experience, b) assess your decision-making skills, and c) understand how you take action based on insights. In your answer:

• Define the problem you were trying to solve.
• Outline the techniques and approach you used.
• Describe the actions you took, and if you had to change course.
• Provide an overview of the results you saw.

For most data analysts, SQL is one of the most important technical skills to have. Since data in organizations are stored in a relational database, removing the need for a data engineer to create a data pipeline.

As a data analyst intern, they simply want to see that you are able to query, modify, aggregate, and process the data as needed. Here are some basic SQL questions you might want to practice with for your next interview.

1. What is a relation?

In SQL, a relation refers to a table in a database. The term originates from the relational model where a relation is a set of tuples with the same attributes. In simpler terms, it’s a collection of rows (or records) with consistent columns. While “table” is the more commonly used term in everyday database operations, “relation” emphasizes the theoretical foundation of relational databases.

2. What is a unique key in SQL?

In SQL, a unique key is one or more columns or fields that identify a record in a database. Tables can have multiple unique keys, which is a difference between unique keys and primary keys. With unique keys, only one NULL value is accepted for the column and it cannot have duplicate values.

3. Differentiate a foreign key from a primary key.

In SQL, primary keys and foreign keys are both fundamental to ensuring data integrity, but they fulfill distinct roles.

A primary key is designed to uniquely identify each record within its own table. It ensures that each entry in a specific column or set of columns is unique and cannot contain NULL values. Each table is restricted to just one primary key, underlining its vital role in record identification.

Alternatively, a foreign key in one table establishes a link to the primary key of another table, solidifying relationships between the two. While a primary key mandates uniqueness within its table, the foreign key ensures that values in its columns correlate to values in the referenced primary key column(s). Unlike primary keys, a table can have multiple foreign keys, and these can contain NULL values, denoting an optional relationship to the referenced table.

4. Select All Flights

More context. Let’s say you work for an airline. The flights table contains information about all flights your airline has booked.

Select all entries from the flights table

5. Top 3 Users

More context. Let’s say you work at a file-hosting website. You have information on the user’s daily downloads in the download_facts table.

Use the window function RANK to display the top three users by downloads each day. Order your data by date, and then by daily_rank

Microsoft Excel is a powerful tool that’s been a staple in the data analytics world for decades. Its versatility in handling data, performing calculations, and creating visualizations makes it an essential skill for any budding data analyst. The following questions will gauge your proficiency with Excel and its various functionalities.

Microsoft Excel is a powerful tool that’s been a staple in the data analytics world for decades. Its versatility in handling data, performing calculations, and creating visualizations makes it an essential skill for any budding data analyst. The following questions will gauge your proficiency with Excel and its various functionalities.

1. How do you enter the current date and time on an Excel sheet?

=NOW() function will return the current date and time. If you just want to enter the current time, but not the date, use the keyboard shortcut Ctrl+Shift+semicolon key, and for the current date without time, press Ctrl+Semicolon.

2. What is a Macro in Microsoft Excel?

A macro is a sequence of performable actions in Excel that have been recorded, saved, and named for easy use in the future.

A macro can then be called on whenever necessary to complete the sequence of actions without the user having to replicate each step manually. This saves valuable time and effort when performing repetitive tasks with larger sets of data. You might, for example, need to manipulate a data set in the same way every week, but it involves 15 steps to complete. By recording a macro, you can manipulate the data in a consistent way extremely quickly, with just a click of a button.

3. How do you build a Pivot Table?

• Click on the Data tab in the ribbon section.
• Then, click the Data Validation button in the Data Tools group
• Navigate to Configurations>Allow>List
• Select an array for the source list

4. Does VLOOKUP use case-sensitive values?

VLOOKUP is not case-sensitive, and will always return the first value of the match, irrespective of the case. In other words, the name Apgar and the acronym APGAR would be viewed as the same by VLOOKUP. It is, however, possible to manipulate VLOOKUP into returning case-sensitive values by using a helper column, or by sorting your data in an ascending or descending order so that the value you want is always the first to be encountered by VLOOKUP.

5. How do you extract the first name from a full name in Excel?

The FIND function will return the numerical location of this target (with the first character of the text being 1). The LEFT function can then extract the number of characters specified by the FIND function from the beginning of the text (i.e. the left).

However, the value returned by FIND will include the space itself, so we need to subtract 1 from the value in order to find the actual ending point of the first name. The formula would look like this:=LEFT(A1,FIND(“ ”,A1)-1).

A second method would separate the first names and last names and deposit them into separate new columns, using the Text to Columns feature found in the Data tab. We covered this earlier under how to split information in a column. The Text to Columns dialogue box will allow you to select the delimiter separating each field (e.g space) and show you a preview of the result. The last step will allow you to choose where you want the result to be displayed.

Data visualization is both the art and the science of representing data in a graphical or pictorial format. It allows complex data to be understood at a glance and can reveal patterns, correlations, and trends that might go unnoticed in text-based data. As an intern, you’ll often be tasked with creating visualizations that communicate your findings effectively.

Here are some questions that you might encounter in your data analyst internship interviews.

1. What makes a good visualization?

You’ll see a variation of this question in every data visualization interview. The question is asked to understand your design philosophy at a basic level and also your ability to design for a specific audience. You can talk about specific characteristics like:

• Color theory and aesthetics
• Matching the visualization to the data / use case
• Ease of deployment and development costs

One key point to hit on: Always bring your response back to the audience. Effective visualizations make data accessible for the target audience.

2. What is a scatter plot? What types of data work best in scatter plots?

A scatter plot is a type of chart that’s used to show correlation between two or more variables. Typically, it’s best used when there isn’t a time element, and can help to show the relationship between the variables, e.g. positive, negative or no correlation. For example, a scatter plot would be an effective choice to show the relationship between height and weight.

3. How do you use color in your visualizations?

You should expect a color theory question. To prepare, practice talking about your favorite color theory techniques. A few to consider would be:

• Using color to highlight important information.
• Using one color to represent continuous data (as well as saturation to represent intensity).
• Avoiding an indistinguishable color palette.
• Limiting too many colors.
• Accessibility for color-blind viewers.

In the world of product management and development, metrics are crucial. They provide insights into how a product is performing, where it’s succeeding, and where improvements are needed. As a data analyst intern, understanding and interpreting these metrics will be a significant part of your role. Here are some product metrics questions asked by real companies to real people.

1. How would you measure the success of the banner ad strategy?

More context. Let’s say that you work for an online media company. The media company is starting to monetize its web traffic and wants to experiment with adding web banners into the middle of its reading content to see if it can monetize effectively.

How would you measure the success of the banner ad strategy?

2. Friend Requests Down

More context. A product manager at Facebook comes to you and tells you that friend requests are down 10%.

What would you do?

3. What are some reasons why the average number of comments per user would be decreasing, and what metrics would you look into?

More context. Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has consistently grown new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing, and what metrics would you look into?

• Comments per active user
• Total number of comments
• Average comments per post
• Total number of active users
• Number of new users

Securing a data analyst internship is a pivotal step in the journey of budding data professionals. While the technical aspects like SQL, Python, and Excel are undeniably important, it’s equally crucial to hone soft skills and cultivate a holistic understanding of the business landscape. From understanding the intricacies of data visualization to grasping the nuances of product metrics, a well-rounded preparation is the key.

Remember, interviews are not just about showcasing what you know, but also about demonstrating your eagerness to learn, adapt, and grow. As the field of data analytics continues to expand and evolve, staying updated and being proactive in your learning will always keep you a step ahead. Whether you’re just starting out or looking to refine your skills, we hope this guide provides valuable insights to help you navigate the interview process and secure that coveted internship position. Best of luck!