Top 13 Data Science Interview Questions and Answers in 2023

This question is essentially asking about your job readiness and how prepared you are to hit the ground running. It aims to assess how much training you may need and how comfortable you can handle challenging or unique data science problems. If you have worked in multiple professional positions as a data scientist, this question is easy to answer. It may be more challenging, however, if you are newly entering the field.

Your experience and educational path may vary — and that’s a good thing. Your unique blend of skills and approach to data science are valid, and while they may not qualify you for every data science job out there, they will help you gain professional experience in the field. Even if you’re just getting started, a portfolio can help you get your skills seen by relevant employers.

If you’re not sure you have enough experience to answer this question, consider enrolling in a data analytics boot camp, where you can gain practical knowledge and applicable skills employers are looking for right now. You’ll also benefit from hands-on experience in the field through group and individual projects that you can add to a personal portfolio.

The Data Analytics and Visualization Boot Camp at Texas McCombs delivers a curriculum that will introduce you to databases, Python programming, business intelligence software, big data analytics, machine learning, Excel functions, visualization, statistics, and more.

Employers want to know why you are interested in their specific company and job. You can answer this question by relating what you know about the company to your passion for data science as a field, and talking about your interest in technology and analytics and why it is exciting to bring your big data skills to this particular employer. As such, it’s important to conduct research into the company and the job listing before the interview to help inform your answer.

For example, you might say, “I love to be able to solve problems by processing, analyzing, and better understanding data. I am looking for a position with a company just like yours, because your [expansive, or privacy-focused, or user-oriented] approach to data is innovative, allowing improvements in product quality and the user experience. I want to work in a position that allows me to grow in my data science career while working on interesting projects just like the work done here.”

This question aims to gauge how well you work with others. As with most interview questions, your answer may vary depending on your experience, but it’s good to prepare a few key anecdotes and examples of your work.

Use the “STAR” technique to describe your previous experience:

• Describe a specific situation
• Talk about your tasks
• Review the actions that you took
• Sum up the results of those actions

One example answer might be the following:

“At my previous job, I was responsible for collecting feedback from customers about how our products fit their needs and report back as to how we could use that information to produce a better customer experience and increase retention. I compiled the data in Excel and produced charts and pivot tables to provide meaningful information about customer demographics, their most common needs and concerns, and the things they liked most about our company. This work helped us to retain and increase our customer base.”

If your data science experience comes through a boot camp, you can easily cater the above answer to any of the projects you worked on throughout the course. As you build your portfolio, it can be helpful to go back to previous projects and update them based on any new knowledge, then explain your role in the projects you’re most proud of as you answer this question.

Here, your potential employer wants to know more about tools and programming languages you have mastered and use frequently. You want to show that you are knowledgeable about tools often used in the field and able to use the company’s preferred coding languages. If you don’t have any previous professional experience, many of the tools covered in a data science boot camp curriculum can get you up to speed and provide you with additional context to answer this question.

For example, your answer might be, “Because this position focuses on presenting statistics and information processed in our data science division, I expect to use SQL queries and database interaction to produce a web-based visualization of customer data. I will use HTML and CSS to design an attractive layout while taking advantage of JavaScript charting to present charts and statistics that make our data science reports compelling and visually accessible. In a visualization project I recently completed, we incorporated large amounts of data, using SQL queries to produce visually stunning images that allowed management to fully understand what decisions customers were making when interacting with our products.”

This question is your opportunity to discuss your theoretical understanding of the field. Discussing a recent project you worked on either in an educational environment or in the workplace can provide additional context for your answer.

You might start with a general overview of how you see the field, answering, “Data science enables companies to understand customers better and give them the features that appeal to them in a product or service. We bring in data through data mining, clean it to ensure it is valid and correct, and then dig into the data, exploring and writing code that enables us to extract the most useful information. We can use that data to create models and visualization, advancing our understanding and analysis to the next level.”

You can then provide additional context by explaining how specific tools and technologies enable organizations to conduct data analysis: “Data science is an interdisciplinary field. We use algorithms, we write code in Python, and we make use of machine learning and an array of high-level tools. With our approach to big data, we can revolutionize or advance any company’s approach to innovation and engagement. In a project I completed recently, we used a data set from the healthcare industry to produce statistical models of the most-wanted services, as well as barriers to access. This kind of report can help healthcare companies increase customer trust and loyalty while also improving the experience for patients.”

If you already work in data science writing code in SQL, Python, or JavaScript, this will likely be an easy question to answer; you can simply pull out some of your projects or show examples of your work.

You might answer this question with the following:

“I created an algorithm to sort through customer data and learn which devices our customers use and how it impacts their experience on our website and online portal. This SQL query interacted with a database of information collected from customers, and the code enabled our company to understand that our portal needed tweaking on certain devices to improve customer engagement. I have also contributed to an open-source big data project, which has been downloaded by users around the world.”

For those that are newer to coding, you may find it more challenging to show your specific technical abilities. Gaining additional practice not only helps you solidify your skills, but gives you the confidence you need to answer technical questions during your data science interview. While a data science boot camp can help you build your portfolio, you can also pursue some key coding projects for beginners on your own to enhance your projects or showcase new skills. This will give you a unique portfolio to present to potential employers and display your passion for the profession. Open source projects in big data are another great way to contribute, work as part of a team and show off your skills.

For this type of question, the interviewer will present a table from a database and ask you to calculate a certain metric. For example, they may ask you to calculate the share of new customers and returning customers over a month-long period as a ratio. Database information may include whether the customer has visited the site in the past — as well as when they created their accounts and what they did when they interacted with the site during the month.

In your response, you would write a SQL query to collect all users, separating customers with a start date in the specified month into the category of new customers. You would use a simple function to produce new users as a share of all customers engaged with the company.

SQL coding is one of the most frequently tested skills in a technical interview in data science, and a data analytics boot camp can prepare you to create an algorithm through hands-on practice and real-world data sets.

Your interviewer may ask about particular types of regression, such as logical regression, linear regression, or Bayesian logic. They may also ask more specific questions, for instance, how regularization works to reduce errors in regression.

You might start by explaining that linear regression is a form of supervised learning; this statistical technique predicts one variable based on a second variable, and logistic regression predicts binary outcomes from a linear combination of variables. Regularization is a special type of regression that regularizes coefficient estimates to zero to minimize variance and decrease sampling error.

This is also a good opportunity to give an example of regression analyses that you have run in the past and cite an example of how you dealt with underfitting or overfitting. Overfitting is one of the most common data analytics errors, and it can indicate that random errors or noise is being captured rather than meaningful material from the data. Data professionals typically use Ridge or Lasso regularization to prevent overfitting in our data, while being careful to steer clear of underfitting, a different error that fails to react to meaningful changes in the data.

Data cleaning increases the accuracy of models used in machine learning while transforming the data into a set that is easy to work with. In fact, cleaning data can take up to 80 percent of the total time required for a project.

Any data brought in by an organization can has the potential to include redundant information, mistaken inputs, or irrelevant data; formatting may be inconsistent, and values may be missing for certain respondents. Python provides libraries like pandas, Matplotlib, Keras, and others that can effectively load large amounts of data, clean it, and produce extensive analysis. Using Python libraries, we can effectively produce data that is ready to use and in good shape for our next projects.

If you are asked this question, be prepared to write simple Python code to show how you would clean a particular data set to prepare it for further analysis and modeling.

Here, your answer may focus on the type of data the interviewer asks you to analyze. If you are analyzing text, Python is the preferred choice because of its high-performance tools and libraries for data analysis and data structures. Python has a fast performance for all types of text analysis, even in large data sets. On the other hand, if you are analyzing machine learning data, R may be the better choice.

Use this question to give an example of work that you have done in Python or R to analyze a data set. Discuss why you decided to use either tool for that data set and show the code you used to produce your final analysis.

Pandas is one of the most widely used open-source Python libraries in data science. Developed for data analysis, the pandas library helps data scientists load, clean, manipulate, prepare, model, and analyze data. In pandas, a data frame represents data in rows and columns. Different data frames can be combined using different approaches, including append, concatenate, and join, depending on the commonalities in the data. Pandas queries can also identify missing values and produce cleaner data for further analytics.

The interviewer may ask you to show an example of Python code that you could use to combine two data frames, clean a data set, or delete specific items from a data frame. If you’re unfamiliar with these operations, or don’t have enough experience using them, The Data Analytics and Visualization Boot Camp at Texas McCombs curriculum covers Python programming, giving you the opportunity to get hands-on practice with the pandas library.

Questions about selection bias helps your interviewer understand how you can best select data to ensure your results are meaningful and effective. You can use examples from your education and/or employment to indicate your practical understanding.

One way to answer this question is to explain that selection bias occurs when you fail to extract truly random data samples:

“Effective data science requires samples to be truly random within the category being studied. When selecting a sample from a database or particular table, we want to ensure that our findings are not skewed by factors we did not incorporate into the analysis. For example, if we overwhelmingly analyze customers in a specific state or of a specific age group, when we intend to analyze all customers that purchased a subscription, our data will not apply to all customers, and our results could be flawed.”

To further contextualize your understanding of selection bias, you can relay an example of a project where you used techniques like weighting, resampling, and boosting to ensure you retrieved a valid sample and avoided skewing the results toward a particular group or set of interests. Then, discuss the different outcomes you observed before and after properly adjusting your data.

Outliers are present in any data set, and if they are not understood and accounted for properly, they can skew averages. In some cases, such as when you are analyzing sports statistics, you may want to find outliers to uncover exceptional players. On the other hand, if you are measuring customer satisfaction, your results may not reflect the average customer if they include one user who spent 100 hours on the platform when the vast majority spend only two or three hours within the same time frame.

As you answer this question, explain how you find outliers and your practical and technical methods. If possible, use an example of identified outliers in your past work. You might say, “I would analyze the raw data to look for general trends, including the emergence of outliers. I would then create histograms, using quartiles and inner and outer fences, to identify outliers and either study them further or exclude them. Points beyond inner fences in either direction are mild outliers, while those beyond the outer fences are extreme outliers. With this basis, we can decide how to work with these outliers amid our overall data set.”