The 9 Types of Data Science Interview Questions You Should Know

Answering data science interview questions isn’t easy if you don’t know what will be asked. If you prepare for the interview by considering the nine types of interview questions listed below, then your chances to succeed increase.

Data analysis vs. data science

Before moving ahead, I’d like to clear some confusion many people encounter:

Is there any difference between data analysis and data science?

Yes, there is.

Data analysis helps to solve known problems. Conversely, data science helps businesses define unknown problems and solve them through the multidisciplinary aspects of existing data.

Data scientists require different tools. For instance, a machine learning model, linear regression model/linear model, hypothesis testing, or data visualization techniques.

Why 9 types of data science interview questions?

I’ve worked in the data science field for several years. I’ve received queries from many students asking for career guidance. This led me to research the foremost issue of aspiring data scientists: how to prepare for an interview.

I found that the most common problem is not having the exact technical expertise to ace a data scientist interview. So, I designed a list of the most commonly asked data science interview questions. Then, I broke it down into nine specific types.

Let’s discuss these types of data science interview questions one by one.

1. Quick math questions

Quick math helps improve arithmetic fluency. It also assists in the development of mental strategies for addition, subtraction, multiplication, division, and the combination of these operations.

Generally, Quick Math questions are simple to answer if you understand basic numerical computations and carefully focus on the details. If the interviewer believes you have higher-level math skills, then the questions asked, then you are given an advanced set of questions. For example, you’ll receive a more difficult calculation if you have a degree in mathematics, statistics, or engineering.

The purpose of quick math questions: The purpose of these questions is to test your problem-solving skills. You must be proficient with them because data science requires you to develop and/or perform numerous calculations.

2. Puzzles

A puzzle tests your levels of logistical thinking. There are many types you could be presented with.

• Logical puzzle
• Relational puzzle
• Number puzzle
• Crossword puzzle
• Word-search puzzle

Per my experience, logical and number puzzles are the most common in data science interviews. Though you should prepare for them first, don’t forget to practice to build a strong skill set.

The purpose of puzzles: The purpose of puzzles in a data science interview is to test your logical aptitude. Again, practice solving various puzzle types before applying for a data science job. Especially if it’s with an organization that hires subject matter experts (SMEs) in the field.

On the other hand, if a company is establishing a data science team from scratch, then puzzles may be eliminated from the interview process. Nevertheless, continue to practice solving puzzles.

3. Guesstimation

Guesstimation questions ask you to estimate a numerical value. To correctly answer these, you need to combine general and background knowledge, problem-solving skills — especially math equations, and logical thinking. Overall, guesstimation questions are answerable if you properly comprehend their context.

For example, if the question is based on the number of red cars in China, then the first thing to do is determine the country’s population. If you have this data on hand, then you could break it down further. This helps produce the estimated value.

The good news is there are no correct or incorrect answers to these questions. The interviewer determines your abilities on how close you get to the actual value.

The purpose of asking guesstimation questions: The reason why recruiters ask these questions is to get a sense of your estimation skills based on knowledge and experience.

4. SQL

Structured query language (SQL) is a programming language. It’s designed to manage data within a relational database management system (RDBMS). It sets protocols to perform multiple queries a user or an application performing interface (API) needs to compile and interpret data. These include actions like select, insert, update, delete, and drop.

Commonly used databases on the market utilize SQL with variations and extensions. These include SQL Server, Oracle, PostgreSQL, MySQL, and MariaDB.

The purpose of asking SQL questions: Information extraction is the first step in data science projects. SQL is usually recommended to perform this action. Thus, you need a strong base in this programming language SQL. Not only does it provide background for the recruiter, but it also makes your job easier.

5. Python or R programming languages

R is an open-source programming language with a large international user base due to its easy-to-use environment. There are several reasons to use it when creating data science programs.

• Flexibility
• Power
• Sophistication
• Expressiveness

Python is another open-source programming language with a strong engineering capability. Most data science industries consider Python the best because it caters to computer scientists.

An additional reason for Python’s popularity is its user-friendly syntax. Thus, it allows data scientists to perform different operations such as web page development, report automation, and the creation of simulations.

Conversely, R is preferred by statisticians and academic researchers. It has the ability to handle large data sets. However, a faster processor is required to compile and interpret the information points.

Despite these differences, the two programming languages are helpful with the concept of data cleaning. Generally, this process detects and corrects inaccurate information from a record set or database. Once found, data cleaning replaces, modifies, or deletes the inappropriate data.

Data cleaning is 20% of the investigation process. The remaining 80% is devoted to the science part. Both R and Python efficiently handle cleaning operations.

As to what language is more important in a data science interview, start with the one you find easier. This depends on your field of study. Once you familiarize yourself with the first language, focus on understanding the second. 

The purpose of asking R or Python questions: The reason behind asking questions based on these tools is to test your proficiency in these languages.

6. Statistics

Statistics helps evaluate the hidden gems data, so stakeholders make the right decisions. It’s not only about data collection and analysis, interpretation, and presentation. Statistics is also a part of everyday life. Whether it’s known or not, everyone uses statistics.

Regardless of the size of your business, gathering and reviewing statistics is unavoidable. You always need to know about the current figures. This allows you to predict future outcomes. You start to understand the power of this science once you regularly use it.

Since statistics is the basis for data science, you must have a comprehensive understanding of its concepts. Carefully study them and commit the information to memory. It is the most important thing to know during the data science interview questions.

The purpose of asking statistics questions: Statistics helps a business forecast sales and find like-minded groups of customers. An interviewer could ask a series of questions based on predictive analysis. Or, they may ask something like, “What is the Central Limit theorem?” to get an idea of your knowledge.

7. Machine learning

Machine learning (ML) is a method that helps systems learn from algorithms to make decisions with little involvement of humans. The concept is utilized in data science. As data is processed by the algorithms, ML starts to improve. It understands what represents good information and what needs to be removed or replaced.

The purpose of asking machine learning questions: The interviewer asks ML questions to test your conceptual understanding. It is important to have a deep comprehension of its processes. However, it’s not necessary to learn all the possible ML algorithms related to data science. Knowledge of the essential ones is sufficient.

8. Tricky questions

Tricky questions confuse you due to the wording of the query. They encourage you to actively listen. Furthermore, tricky questions are designed to examine your attention to detail. Careful practice and consideration of these queries help you easily answer them.

The Purpose of Asking Tricky Questions: The reason an interviewer asks a tricky question is to review your critical thinking ability. Answering these is sometimes difficult because you might not get enough time to respond. The best way to tackle tricky questions is to carefully focus on what’s being said.

9. Probability

Probability is important to both data science and statistics. A branch of mathematics, it helps scientists calculate how likely an event could occur. The probability is a number between zero and one. The former indicates the impossibility of the event, while the latter indicates its certainty.

Here’s a basic example of probability:

To calculate the chances to become a data scientist, you divide the number of related skills you have with the total provided in the job description. Additionally, you ignore factors such as competition or years of experience. If the probability comes out to be greater than 75%, then you should start searching for a position.

Granted, the above situation isn’t the purest form of probability. Truth be told, the calculation of this concept within data science is more involved. Sometimes, hundreds of data points are compiled to produce the results. On top of this, probability algorithms need to be tweaked to obtain the most accurate results.

The purpose of asking probability questions: The science of probability is a helping hand for businesses. They collect a lot of data as a solution to their problems. However, if they don’t factor in probabilities, then they don’t know how to proceed. Hence, the reason why an interviewer could ask you several questions related to the topic.

Non-technical data science interview questions

Along with the data science interview question types mentioned above, you are also asked to answer non-technical questions. This is essential to know as candidates tend to ignore them, especially if they apply for high-tech jobs.

Yet, this is an incorrect approach. The interviewer is likely to ask non-technical questions to assess subjects like your long-term objectives, your interests, and your working style.

The below questions are important from an employer’s point of view. Therefore, if you don’t answer them appropriately, then you could face unfavorable results. So, consider answering these questions carefully.

1. Why did you leave/want to leave your last/current company?

The majority of candidates get stuck when they face this question. An incorrect answer could put your potential employment in jeopardy. In general, people want to leave their jobs mostly because of the three reasons:

• Not happy with their boss
• Not happy with their job
• Has not performed based on company expectations

Avoid these reasons and answer in a positive way. For instance:

• Say you’re passionate about data science and worked hard to reach this goal. Say, “I am supremely confident that I can obtain a data science job. This is the reason I am leaving/left my previous company.” If you speak this with confidence, then the interviewer has more of a reason to believe you.
• Tell them your skills aren’t being fully utilized, and you believe something better is on the horizon.
• Inform them that you’re looking for new opportunities and challenges.
• Let them know you want an opportunity to work with technologies the company currently utilizes.

2. Why are you the best candidate for the given job title?

Here, the interviewer wants to know about your specific experiences related to the listed job. Case in point, if you’ve applied for a data scientist position at an eCommerce company, then mention your past projects related to the sector.

For this purpose, I suggest you check out Kaggle competitions. Participate in competitions that are relevant to the company you want to work for. Make sure you do this for every company; it will help you build your data science portfolio. You should also read my blog post on How to Make Your Own Data Science Portfolio for more details.

Along with the above strategy, I suggest you do one or more of the below tasks and mention them in your interviews.

• Create a data science blog and update it regularly.
• Create a YouTube channel based on data science instructional videos.
• Create a podcast where you interview experienced and aspiring data scientists.
• Write an eBook and publish it on Kindle and other platforms. The goal is to reach readers rather than make money.
• Start commenting and answering questions on Stack Overflow and GitHub.
• Search for speaking opportunities at local data science events or conferences. If none are available, then ask questions during sessions. Furthermore, ask the organizers if they need volunteer speakers.

When you start working on some of these items, you’re more likely to get through the interview process. Look into blogs, self-published books, and Q&A sessions to start.

3. Are you a team player?

This question has several formats.

• Can you tell me about any of your team projects?
• Can you share your teamwork experience?
• Have you ever worked with individuals in other teams?

You need to understand that data science projects aren’t completed without cooperation with other teams or departments. On the other hand, you sometimes work alone or as a project manager. Therefore, the best answer is to mention both your team and leadership skills.

4. Why do you want to work for us?

Every employer wants to retain their workers for a long period. This saves time and hiring costs. So, to properly answer this question, research the company’s history and culture. Then, try to match your objectives with the organization.

Let’s say you’re interviewed for an organization that helps diabetes sufferers. An example answer is, “I know how hard it is for diabetics to maintain a healthy lifestyle. My [friend/family member] is also diabetic. To be a part of an organization like yours allows me to do my part to help.”*

*Of course, don’t say this if your friends or family members aren’t diabetics. Instead, speak about the epidemic as a national issue.

Next, go into the skills that match the open position. Share some ideas that could address the company’s known pain points.

5. What are your salary expectations?

Perhaps the high pay of data scientists has encouraged you to seek the position. Rather than initiate salary expectations during the interview, provide a range established from previous research. Try to determine the salaries of existing company data scientists. Another option is to search sites like the Bureau of Labor Statistics (BLS) for estimates.

Final advice 

1. Feeling confident within the data science field takes time.

You could feel frustrated because of the complexities of data science. However, don’t feel hopeless. No one knows everything. No one has the capacity to solve every type of data science problem. 

To be honest, many data scientists lack confidence in their skills. It’s not because they lack the knowledge. They understand the rapidly changing characteristics of data.

Plan your step-by-step learning by setting day-to-day goals. Try your best to accomplish them to boost your self-confidence. Don’t think about immediate outcomes. You will see the results as time passes. 

Remember that hard work pays off.

2. Perfection of your knowledge is highly required and useful.

You might have heard the saying, “Jack of all trades, master of none.” To become a data scientist, you must be the master of the things you know and forget about the things you don’t know. 

Perfection of basic concepts is more important than knowing difficult machine learning algorithms. In other words, you need the ability to apply what you know to real-life problems.

My course Guide to Careers in Data Science – Interview Hacks details the minimum number of topics to learn to become a data scientist. You’ll find answers to all your queries regarding data science. This includes example questions and answers, mantras for guaranteed success, the dos and don’ts of preparation, and much more.

3. Don’t learn different ways to do a single operation.

Since you want to start a career in data science, It’s essential to spend your time learning wisely. Most aspiring data scientists believe they must learn every way to solve a problem. This is the wrong approach.

For example, you can create a bar chart with the qplot function of the ggplot2 package in R as well as with the ggplot function. There’s no need to learn both functions. Simply focus on one and master it.

Job interviewers rarely ask about different ways to create a bar chart. The important thing is to understand when to create one.

4. Do not make decisions ONLY on the suggestions of others. Use your intuition.

Though I’m suggesting many things to you, don’t make any decisions based only on what I say. It doesn’t mean you shouldn’t listen to others. Yet sole dependence on what others say may not be helpful. In simpler terms, before you make any decisions, evaluate the suggestions and compare them with each other.

I say this because I listened to the suggestions of peers in my career. Once I evaluated the suggestions, it helped me make better decisions. As a result, I have years of experience and have helped 35,000+ people in my two courses on data science.

Remember one thing; only you can make the right decision for yourself.

5. Don’t concentrate on Deep Learning to start your career.

Keyan Halperin, the senior data scientist at Facebook, says, “You can absolutely get a data science job without much (or any) experience with deep learning.” This is the answer to “How important is ‘Deep Learning’ for data science? Can you get a data science-related job with only experience in Machine Learning?” on Quora.

He also said that it depends mainly on the data science position. However, in general, Deep Learning isn’t necessary.

So, if you’ve thought about Deep Learning, then please drop that idea. Your first priority should be getting into the data science field. Then you can move ahead in the industry.

Conclusion (HURRAY!)

This was long, but we made it. I am happy that you reached this point.

The nine types of data science interview questions:

1. Quick math questions
2. Puzzles
3. Guesstimation
4. SQL
5. R programming or Python
6. Statistics
7. Machine learning
8. Tricky questions
9. Probability

Non-technical data science interview questions you should prepare for.

1. Why did you leave your last company? Why do you want to leave your current company?
2. Why are you the best candidate for the given job title?
3. Are you a team player?
4. Why do you want to work for us?
5. What are your salary expectations?

Advice

1. Feeling confident in data science takes time.
2. Perfection of your knowledge is highly required and useful.
3. Do not learn different ways to do a single thing.
4. Do not rely only on others’ suggestions. Use your intuition.
5. Don’t concentrate on Deep Learning to start your data science career.

If you want to know about data science in a layman’s way before making any decision, then check out my course Data Science Approach from Scratch: An easy explanation. Feel free to enroll, as it also comes with a 100% money-back guarantee.

Good luck with your journey toward data science.