Key Data Science Interview Questions: An Inside Look

Data science interviews are some of the most challenging in any industry. It’s not hard to see why. There are thousands of search results for “data science interview questions” and lots of candidates helping each other out on message boards, social networks and job review sites. The interview process can be long, and data scientists need to understand the business challenges of the company you’re interviewing for in addition to the technical. For your interview, you’ll need to prepare for the possibility of completing a coding challenge or technical interview.

Therefore, preparation is paramount. To ensure that you’re prepared for a data science interview, it’s essential to spend time understanding what past interviews for data positions entail. In addition, preparation involves reviewing common data science interview questions that companies typically ask, having an idea of how the teams are structured, and the backgrounds of the stakeholders who will interview you and make the hiring decisions.

This article will introduce some of the most common data science interview questions asked during an interview, as well as some helpful insights to help make sure you arrive at your interviews more prepared than the other applicants.

For easier classification, the questions will be broken down into several categories. Each category will cover some commonly asked data science questions within that section.

During a data science job interview, a mix of technical and structural questions will likely be asked. The goal from the company’s standpoint is to test the applicant’s knowledge of data science and their fit for the company. After reviewing these questions, you’ll better understand how to approach the data science interview questions related to the ones listed below.

Structural/Cultural Fit Interview Questions

Companies need to know that the data science professionals they are investing in will complement the skill set of the existing team. Therefore, before reviewing your technical expertise, these questions are commonly used to open up an interview. The aim is to allow the interviewer to know you better, understand your personality and assess your strengths and weaknesses.

Tell us about yourself. Every data science master’s student has experienced this question during their career. The interviewer on the company’s data science team has already reviewed your resume but is looking to test how well you can sell an idea, whether you’ve read and understood the job description, and if you’re prioritizing your most relevant expertise to their needs. For a data science job, this question is a great opportunity to discuss some of your most notable accomplishments in previous jobs, allowing you to introduce your data analysis expertise found in the job description.

Tell us why you chose a career in data science. This question is asked to test how committed you will be to the role and how long you plan to spend in the field. It’s sometimes asked of candidates whose resumes show an unconventional career change or gaps. This question allows you to have a second opportunity to point to an accomplishment in your resume that solidified your commitment to the field, and also an opportunity to tell a human story about how you came to be a data scientist.

Take time to prepare these answers before you meet with the hiring manager, carefully consider how you became interested and started in data science, and provide your perspective on how the field has impacted you and helped others. Prepare to cite engaging examples and emphasize your educational background. Too many data science professionals lean heavily on the knowledge of the field at the expense of showing their ability to be personal and work with others.

Statistical Interview Questions

Depending on the job listing, data science interview technical questions typically cover the following subjects: data analysis, machine learning, statistics and artificial intelligence. This is because certain jobs emphasize some data analytics skills over others.

These questions aim to test an applicant’s skill in various applied domains and their familiarity with basic concepts. The statistics questions revolve around applying mathematical and logical principles to tackle real-world problems.

Provide a definition and an example of linear regression. This is one of the most common statistics questions usually asked, and one that a candidate at this stage in their career may have seen before. The purpose of this question is to assess your communication skills of how well you understand and explain basic concepts.

You should be able to succinctly explain that linear regression is simply a basic statistical method that involves finding the relationship between two continuous variables. Communicate that the factor that is being predicted (normally y) is known as the dependent variable while the predictor (normally x) is known as the independent variable.

A few other questions related to linear regression and logistic regression are usually asked in data science interviews. You should also prepare to answer the following:

• How would you evaluate a logistic regression model?
• State an example of how you’ve used a logistic regression model recently.
• How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple linear regression?
• When should you use classification or regression?

These questions are designed not only to test your knowledge of the data science field, but also to test your thought process and application of real-world cases.

Machine Learning and AI Interview Questions

Depending on the position (these questions are common in a data engineer role) and the company, these sets of questions are aimed at assessing your understanding of machine learning and artificial intelligence related concepts.

Describe the difference between supervised and unsupervised learning. Supervised and unsupervised learning are basic concepts of machine learning so you’ll be expected to answer this technical question in detail.

Make sure you can describe that, in supervised learning, the machine is trained with labeled training data. A supervised machine learning algorithm includes regression, decision tree, neural network and classification.

In unsupervised learning, the output isn’t as obvious. This is because we have unlabeled training data. Here, the machine learning model works on its own to discover patterns and insights that were previously undetected. Unsupervised machine learning algorithms can apply techniques like clustering and dimensionality reduction to describe data.

Knowing the difference between machine learning and deep learning. Some of the biggest companies in tech build their platform on machine learning algorithms. Many of their products focus on curation or customization, using machine learning to surface content or products that (based on existing data sets) a user is more likely to engage with.

These are prime examples of machine learning. Companies like Pinterest, Facebook, and Yelp use machine learning algorithms extensively and want to hire data scientists who have a firm understanding how to make their core product continue to drive (and increase) engagement. As the cost of developing machine learning algorithms goes down, its adoption will also be accessible to emerging startups.

Deep learning lives as a subset of machine learning. Companies like NVIDIA are investing heavily in GPUs and system architectures for consumer hardware and are also deeply embedded in self-driving car technologies. You should be prepared to give examples of companies that are building the next generation of technology using deep learning as well as its relationship to machine learning.

Be prepared to describe some steps for data wrangling and data cleaning before applying machine learning algorithms. Some of these steps include:

• Structuring data to organize and map unstructured data into a usable format
• Identifying missing values so you know where your data mapping will fall short, and what data needs to be retrieved
• Removing duplicate inputs so sloppy datasets aren’t present after the transfer
• Implementing an outlier detection process

Handling missing or corrupted data in a dataset. There are two methods often used when handling missing or corrupted data: elimination and imputation. Neither of these methods for replacing data is without its downsides. Expect to know the potential problems with using either elimination or imputation to resolve missing data in your dataset.

Elimination may remove the missing rows or columns so that we can work with the dataset. If the dataset is large with little to no missing data, this may be a safer option. For smaller datasets, this method could hurt your data’s accuracy when modeling.

Imputation attempts to fill in missing data, but know that it may introduce selection bias into the data set which would be detrimental to developing accurate insights.

Describe cross-validation. Cross-validation is a technique for evaluating machine learning models by training several machine learning models on subsets of the available input data and evaluating them on previously unseen subsets of the input data. Prepare to explain the process of using training data and test data to resolve problems with overfitting.

Know the Strengths of Programming Languages

Data scientists and analysts use a number of programming languages like Python, R, and SQL. These coding interview questions are meant to assess your practical skills and how well you can work with specific programming languages. You should be able to identify the use cases for Python and R. Most data scientists make use of Python or R for data analytics and modelling. You should also be able make a case for what language would be the best for specific purposes and the libraries/modules. For example, you could argue that Python would be traditionally better for text analytics as it uses the pandas library which makes performing analytical functions easier.

Behavioral Interview Questions

With behavioral interview questions, the interviewer wants to understand how you handled real-world scenarios in the past, how you were able to identify and complete a business challenge with a team, and the resulting benefits to the company from your work.

Prepare to walk the interviewer through how you solved real-world problems using some past work in the data science field, coursework, or a capstone project.

Combine Preparation and Education to Land a Job in Data Science

Answering an interview question well begins with practiced expertise in the field. In order to differentiate yourself in a data science interview and advance further into your career, a foundational knowledge in data science is required. Preparing to interview for a higher level data science job requires preparation that starts in an upper level data science course.

The online Master of Science in Data Science at the University of Virginia School of Data Science provides a cutting-edge data science curriculum that prepares graduates to join challenging, in-demand roles as advanced data science professionals in the workforce.

Click to learn more about how the online M.S. in Data Science program challenges students to make sense of exciting datasets and how we help our graduates turn insights into actions.