Data Science Interview Questions

Data Science interview processes can vary depending on the company and industry. Typically, they will include an initial phone screening with the hiring manager followed by one or several onsite interviews.

You will have to answer technical and behavioral data science interview questions and will likely complete a skills-related project.

Data science interview questions will test your knowledge and skills around:

• Statistics
• Programming
• Mathematics
• Data modeling

Employers will also be assessing your technical and soft skills and how well you would fit in with their company.

By preparing some common data science interview questions and answers, you can enter the interview with confidence. There are a few different types of Data Scientist questions that you can expect to encounter during your data science interview.

Employers are looking for candidates who have a strong knowledge of data science techniques and concepts. Data-related interview questions will vary depending on the position and skills required.

Here are some examples of sample data-related interview questions and answers:

Question: What is the difference between supervised and unsupervised learning?

Answer: The biggest difference between supervised and unsupervised learning involves the use of labeled and unlabeled datasets. Supervised learning uses output and input data that is labeled, and unsupervised learning algorithms don’t.

Another difference is that supervised learning has a feedback mechanism while unsupervised learning does not. Finally, commonly used supervised learning algorithms include logistic regression, support vector machines, and decision trees, while unsupervised learning algorithms are k-means clustering, hierarchical clustering.

Question: What is the difference between deep learning and machine learning?

Answer: This question can be difficult to answer clearly because there is obviously some overlap here. Start by explaining that deep learning is essentially a subfield of machine learning and that both fall under the umbrella of artificial intelligence.

Where machine learning uses algorithms to analyze data and ultimately learn to make decisions based on what it brings out of the data, deep learning layers those algorithms to create artificial neural networks capable of learning and making informed decisions.

How do you differentiate between a type I vs type II error?

Can you provide an example of a data set with a non-Gaussian distribution?

Can you explain the difference between the K Nearest Neighbors (KNN) algorithm and k-means clustering?

What’s your approach to create a logistic regression model?

What is the 80/20 rule? How is it important to model validation?

Please explain the difference between L1 and L2 regularization methods?

Before applying machine learning algorithms, what are the steps for data wrangling and data cleaning?

Can you explain the difference between a histogram and a box plot?

Can you explain what a false positive and a false negative are? What would you say is better to have: too many false positives or too many false negatives?

In your opinion, what is better: An ensemble of 50 small decision trees or a large one?

Can you think of a data science project at our company that would interest you?

Can you please think of a few examples of best practices in data science?

You should also be able to define and explain a number of terms, including:

Technical skills questions in a data science interview are used to assess your data science knowledge, skills, and abilities. These questions will be related to the specific job responsibilities of the Data Scientist position.

Technical data science interview questions may have one correct answer or several possible solutions.

Examples of technical data science skill interview questions include:

Question: What are the top tools and technical skills for a Data Scientist?

Answer: Data science is a highly technical field and you will want to show the hiring manager that you’re adept with all of the latest industry-standard tools, software, and programming languages.

Out of the various statistical programming languages used in data science, R and Python are most commonly used by Data Scientists. Both can be used for statistical applications such as creating a nonlinear or linear model, regression analysis, statistical tests, data mining, and more. Jupyter Notebook is often used for statistical modeling, data visualizations, machine learning functions, etc.

Of course, there are a number of dedicated data visualization tools used extensively by Data Scientists, including Tableau, PowerBI, and plotting packages in Python such as matplotlib, seaborn, Bokeh, and Plotly. Data Scientists also need plenty of experience using SQL and Excel.

Top Data Science Tools and Skills

Your answer should also mention any specific tools or technical competencies demanded by the job you’re interviewing for. Review the job description and if there are any tools or programs you haven’t used, it might be worth becoming familiar with before your interview.

Question: How do you treat outlier values?

Answer: Some types of outliers can be removed. Garbage values or values that you know cannot be true, can be dropped. Outliers with extreme values far outside the rest of the data points clustered in a set can be removed as well.

If you cannot drop outliers, you could reconsider whether you chose the right model, you could use algorithms (like random forests) that won’t be impacted as heavily by the outlier values, or you could try normalizing your data.

Have you worked on a data science project that required a substantial programming component? What did you take away from the experience?

Describe how to effectively represent data with five dimensions.

You need to generate a predictive model using multiple regression. What’s your process for validating this model?

How do you ensure that the changes you’re making to an algorithm are an improvement?

Please provide your method for handling an imbalanced data set that’s being used for prediction (i.e., vastly more negative classes than positive classes).

What’s your approach to validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?

You have two different models of comparable computational performance and accuracy. Please explain how you decide which to choose for production and why.

You are given a data set consisting of variables with a substantial portion missing values. What’s your approach?

Along with testing your data science knowledge and skills, employers will likely also ask general questions to get to know you better. These questions will help them understand your work style, personality, and how you might fit into their company culture.

Personal Data Scientist interview questions may include:

Question: What makes a good Data Scientist?

Answer: Your response to this question will tell a hiring manager a lot about how you see your role and the value you bring to an organization. In your answer, you could talk about how data science requires a rare combination of competencies and skills.

A good Data Scientist needs to combine the technical skill needed to parse data and create models with the business sense necessary to understand the problems they’re tackling as well as recognize actionable insights in their data.

Please tell me about yourself.

What are your best qualities professionally? What are your areas of weakness?

Is there one Data Scientist you admire most?

What inspired your interest in data science?

What unique skills or characteristics do you bring that would help the team?

What made you decide to leave your last job?

What level of compensation are you expecting from this job?

Do you prefer to work alone or as a part of a team of Data Scientists?

Where do you see your career in five years?

What’s your approach for handling stress on the job?

How do you find motivation?

What’s your method for measuring success?

How would you describe your ideal work environment?

What are your passions or hobbies outside of data science?

Leadership and communication are two valuable skills for Data Scientists. Employers value job candidates who can show initiative, share their expertise with team members, and communicate data science objectives and strategies.

Here are some examples of leadership and communication data science interview questions:

Question: What do you like about working on a multi-disciplinary team?

Answer: A Data Scientist collaborates with a wide variety of people in technical and non-technical roles. It is not uncommon for a Data Scientist to work with developers, designers, product specialists, data analysts, sales and marketing teams, and top-level executives, not to mention clients.

Choose an example of a situation where you reported to the highest-level people in a company to show not only that you are comfortable communicating with anyone, but also to show how valuable your data-driven insights have been in the past.

Can you think of a professional situation where you had the opportunity to demonstrate leadership?

What is your approach to conflict resolution?

What is your approach for building professional relationships with colleagues?

What’s an example of a successful presentation you gave? Why was it so compelling?

If you are talking to a colleague or client from a non-technical background, how do you explain complex technical problems or challenges?

Please recall a situation when you had to handle sensitive information. How did you approach the situation?

From your own perspective, how would you rate your communication skills?

With behavioral interview questions, employers are looking for specific situations that showcase certain skills. The interviewer wants to understand how you dealt with situations in the past, what you learned, and what you are able to bring to their company.

Examples of behavioral questions in a data science interview include:

Question: Do you recall a situation when you had to clean and organize a big data set?

Answer: Studies have shown that Data Scientists spend most of their time on data preparation, as opposed to data mining or modeling.

Data cleaning is also one of the most important steps for any company. So you should take the hiring manager through the process you follow in data preparation:

• Removing duplicate observations
• Fixing structural errors
• Filtering outliers
• Tackling missing data
• Data validation

Think back to a data project you have worked on where you encountered a problem or challenge. What was the situation, what was the obstacle, and how did you overcome it?

Please provide a specific example of using data to elevate the experience of a customer or stakeholder?

Please provide a specific situation where you met a goal. How did you achieve it?

Please provide a specific situation where you failed to meet a goal. What went wrong?

What’s your approach for managing and meeting tight deadlines?

Can you remember a time you faced conflict at work? How did you deal with it?

To give you an idea of some other questions that may come up in an interview, we compiled a list of data science interview questions from some of the top tech companies (Amazon, Google, Facebook, and Microsoft).

What’s the difference between support a vector machine and logistic regression? Please provide examples of situations where you would choose to use one rather than the other.

If removing missing values from a dataset causes bias, what would you do?

When looking at a product’s health, engagement, or growth, what metrics would you assess?

When trying to address or solve business problems related to our product, what metrics would you assess?

How do you judge product performance?

How do you know if a new observation is an outlier?

What is the bias-variance trade-off?

What is your method for randomly selecting a sample from a product user population?

What is your process for data wrangling and cleaning before applying machine learning algorithms?

How do you differentiate between good and bad data visualization?