How to Prep for Data Science Interview (Prep Tools + Q&A)

How to Prep for Data Science Interview (Checklist)

1. Research the company and learn what they are about
2. Research who will be interviewing you 
3. Research the job description and make sure you know what the duties are responsibilities are 
4. Identify the job’s success drivers
5. Match your data science skills and experience to the success driver of the job’s three most important responsibilities
6. Practice answering commonly asked interview questions
1. Top 10 Interview Questions
2. Common Interview Questions
3. First 90 Days Interview Questions
4. Qualification Interview Questions
5. Job Description Knowledge Interview Questions
7. Practice answering data science interview questions
1. Common data science interview questions
2. Fundamental data science interview questions
3. Technical data science interview questions
8. Select, practice, and master three questions to ask the interview
1. Master the One Question you must ask the interviewer
2. Select and master a second question to ask the interviewer based on what is important to you
3. Select and master a third question to ask the interviewer based on what is important to you
9. Make your social media profiles interview ready
1. Polish your professional profiles on sites like LinkedIn and Indeed
2. If you have a Website portfolio make sure it is updated
3. Make sure your social media profiles are consistent with what you say and portray in your data science interview
4. To avoid having an interviewer’s opinion of your social media accounts impact their decision to hire you make your social media profiles, images, videos, and messages private
10. Prepare your data science interview materials
1. Print at least two copies of your resume (at minimum one for you and one for the interviewer)
2. Print letters of recommendations
3. Have a list of links to example projects ready to go
11. Confirm what you will wear to your interview
1. Select a backup interview outfit in case something happens to your first choice
2. Do not apply cologne or perfume on the day of your interview (you do not want to accidentally give the interviewer an allergic reaction)
3. Confirm what you will eat prior to your interview to ensure you have healthy sustainable energy to perform your best (make sure to avoid strong smelling foods)
12. Confirm your interview logistics
1. To ensure you arrive to an in-person interview 10 minutes early know where you are going, how long it will take you to commute, what the parking situation is like, and how-to access the office within the building (if building check-in is applicable)
2. If it is a phone or video interview confirm a quiet location with an electrical outlet where you will have your interview
3. If it is a video interview complete a virtual video interview set-up checklist to make sure you’re comfortable with the video interview format and come across professional
13. Send a thank you 
1. Email the interviewer a thank you that follows up on interview details
2. Standout by mailing the interviewer a “handwritten” thank you note

Data Science Interview Questions and Answers

The questions asked in data science interviews vary based on the company and its tech and data stack, but there are common themes. Here are five of the most common questions that you might be asked in your data science interview:

• What is your process to solve a problem?
• How would you find and correct errors in data?
• What is the most important skill for a data scientist?
• How have you extracted insights from data in the past?
• Tell me about a time when you had to analyze complex data.

Data Science Interview Question: Solving Problems

Data scientists are constantly solving problems. This means that you need to be comfortable thinking critically and coming up with creative solutions. In your interview, your interviewer will want to see how you approach solving problems. They might ask you to walk through how you would go about solving a specific problem.

When your interviewer asks, “How do you go about solving a problem?“

An example of what you might say could go something like this:

“Well, first I would try to understand the problem. I would read through the data and see if there are any patterns that I can spot. After that, I would come up with a plan of action and start testing different methods until I found a solution. If I hit a roadblock, I would take a step back and try to find a different way to approach the problem.”

Data Science Interview Question: Finding Errors in Data

A big part of being a data scientist is identifying and correcting errors in data. In your interview, your interviewer will want to see how you handle this type of situation. They might ask you to walk through how you would find and correct errors in data.

When your interviewer asks, “How would you find and correct errors in data?“

An example of what you might say could go something like this:

“Well, I would start by checking the data for inconsistencies. I would look for any values that seem out of place or that don’t match the rest of the data. After that, I would try to correct the errors. If I wasn’t able to correct them, I would document them so that someone else could take a look at them.”

Data Science Interview Question: The Importance of Skills

In any field, certain skills are more important than others. When it comes to data science, the most important skill is critical thinking. Critical thinking is the ability to think outside the box and come up with innovative solutions. In your interview, your interviewer will want to see how you approach challenges and whether or not you have the critical thinking skills necessary for their team. They will likely ask you to describe your experience with solving complex problems in the past.

When your interviewer asks, “What is the most important skill for a data scientist?“

An example of what you might say could go something like this:

“Well, I double majored in economics and mathematics and minored in psychology. My education helps me to approach problems from different angles, depending on how the details are presented. In the past, I have found that my ability to come up with solutions outside of conventional methods has been very beneficial. I would also say that the ability to communicate complex ideas in a clear and concise manner is a very important skill for a data scientist to have.”

Data Science Interview Question: Extracting Insights

A data scientist’s job is to extract insights from data. This means being able to see patterns in data and identify trends. In your interview, your interviewer will want to see how you have done this in the past. They might ask you to walk through an example of how you were able to extract insights from data.

When your interviewer asks, “How have you extracted insights from data in the past?“

An example of what you might say could go something like this:

“Well, I usually start by looking at different graphs and reports that summarize the data. After that, I try to look for patterns and see if there are any interesting relationships between different variables. If I find anything noteworthy, then I test it with more data or implement it. Finally, I try to communicate these insights so that other people can understand them.”

Data Science Interview Question: Analyzing Complex Data

A data scientist is someone who is able to analyze complex data. In your interview, they will want to know how you have done this in the past. Your interviewer is almost guaranteed to ask you a question about a complex dataset and have you walk through your thought process.

When your interviewer asks, “Tell me about a time when you had to analyze complex data.”

An example of what you might say could go something like this:

“I once worked with a data set that was over 50GB. The dataset had several different tables, so it wasn’t easy to get an overview. I started by creating pivot tables to help me understand the distribution of values within each column. After that, I used VLOOKUP to find specific values and spot any missing or inconsistent data. I created pie charts to visualize the data. Then to communicate the data to the team I created a concise report with all my findings.”

Data Science Interview Question: Specific Fundamental and Skills Assessment

In your interview, your interviewer will want to know your skill level. To assess skill level they will ask fundamental questions and present problems to see how you solve them. Below are a list of data science interview questions, complied by iN2iTY Lab Chief Data Scientist and Co-founder, Steve Nouri.

Fundamental data science interview question examples:

• Python or R – Which one would you prefer for text analytics? 
• What is Cluster Sampling? 
• Can you cite some examples where a false positive is important than a false negative? 
• What is linear regression? 
• How regularly must an algorithm be updated? 

Technical data science interview questions examples:

• After studying the behavior of a population, you have identified four specific individual types that are valuable to your study. You would like to find all users who are most similar to each individual type. Which algorithm is most appropriate for this study?
• [0, 0, 0, 1, 1, 1, 1, 1] are the eight actual values of the target variable in the train file. What is the entropy of the target variable?
• In any 15-minute interval, there is a 20% probability that you will see at least one shooting star. What is the probability that you see at least one shooting star in the period of an hour? 
• A certain couple tells you that they have two children, at least one of which is a girl. What is the probability that they have two girls? 
• You are given a dataset on cancer detection. You have built a classification model and achieved an accuracy of 96 percent. Why shouldn’t you be happy with your model performance? What can you do about it? 

How to Prep for Data Science Interview (Tools)

Your interviewer will want to know how experienced you are. A large part of your data science interview will involve providing examples of how you solve problems and what methods and tools you use to solve problems.

When preparing for your data science interview be prepared to answer questions about the following tools:

• dplyr
• Geopy
• ggplot2
• httr
• PyTorch
• TensorBoard
• Keras
• Pandas
• NumPy
• Matplotlib
• Radar chart
• Rspotify
• SciPy
• Seaborn
• Scikit-Learn
• Surprise
• Tidyr
• Wordcloud
• XGBoost

Data Science Interview Cheat Sheet

Prepping for your data science interview can be overwhelming. To make the process easier here are a few data science interview cheat sheets to help:

• Data Science Interview Cheatsheet: SQL
• Data Science Interview Cheatsheet: Statistics and Probability
• Data Science Interview Cheatsheet: Neural Networks
• Data Science Interview Cheatsheet: Math
• Data Science Interview Cheatsheet: Machine Learning
• Data Science Interview Cheatsheet: Machine Learning and Big Data
• Data Science Interview Cheatsheet: Deep Learning
• Data Science Interview Cheatsheet: Supervised Learning
• Data Science Interview Cheatsheet: Unsupervised Learning
• Data Science Interview Cheatsheet: Computer Vision
• Data Science Interview Cheatsheet: Natural Language Processing

Data Science Interview Prep Resources

Want extra help to ace your data science interview? Here are a few great resources to help you nail the details of technical data science interview questions:

• Cracking the data science interview (Help from a machine learning researcher)
• Becoming Human (Data Science Tutorials, Downloads, Job Board, and More)
• Data Science Interview Question Bank
• Free Data Science Interview Prep Course
• 50 Data Science Interview Questions and Answers (with Video)

How to prep for data science interview (FAQs)

Conclusion

Hopefully, this article has provided you with helpful information to help prepare for your data science interview.

Data science interviews are primarily testing your skill level, problem-solving abilities, and ability to communicate. To boost your chances of acing your interview and getting the job offer use the concepts and tools in this article to prepare and practice for your data science interview.

What did you find most helpful in this blog post? Leave a comment below and let us know. If you have any questions, don’t hesitate to reach out!

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