There’s no getting around it—interviews are tough, especially if you’re entering a new field for the first time. If you’ve been invited for a data science interview, the questions you’ll face will, of course, vary depending on the position you’ve applied for. Regardless of the role, there are several key areas where you’ll need to prove your worth. In this post, we take a look at these.
Since data science is a relatively new field, interviewers will rarely expect you to know everything. That’s why we’ve focused on helping you to cultivate the right mindset for showing off your best side. Knowing your facts is important, but so is being able to spot probing questions!
To make things easier to navigate, we’ve broken this post down into the following categories. By the time you’ve finished reading, you should know how to turn even the most challenging data science interview questions to your advantage!
- Introductory questions
- Statistics interview questions
- Programming interview questions
- Data modeling interview questions
- Machine learning and AI interview questions
1. Introductory questions
Introductory questions (or ‘ice-breakers’) can appear deceptively simple. However, an interviewer will usually be probing deeper than you think:
“Tell us a little bit about yourself”
A classic interview opener. But there’s more to it than meets the eye! If you’ve made it this far, the interviewer will have already seen your resumé. Rather than regurgitating what they already know, tell them something new. Have you always worked in data science? If not, how did you get into it? What excites you about the field? This question is perfect for flaunting your passion. Highlight which aspects of data science interest you most, before touching on your practical skills. Whenever possible, link your response to the current role. Keep in mind what the interviewer needs to know, rather than getting side-tracked (which is easy to do!)
“Why is data science important to you?”
Another straightforward question that is less innocuous than it seems. This is a veiled way of testing your understanding of the field. As a relatively new discipline, data science attracts a lot of ‘wannabes’. Despite having laudable skills, some may lack the multidisciplinary expertise needed for certain roles. This is your chance to prove that you are not one of these candidates.
For instance, discuss the value of data insights to everyone from advertisers to healthcare providers. Touch on data mining, data modeling, machine learning, computer programming, and statistical analysis. If you can, tie your response to the job in question. This will show the interviewer that you know your stuff. It’ll also prime them for any follow-up questions they might have (spoiler alert: they will definitely have follow-up questions!)
2. Statistics interview questions
Statistics define how we manipulate and read data. Understanding its benefits and pitfalls is the only way to create accurate predictions (a data scientist’s ultimate goal). Explaining tricky concepts in simple terms is also key for showing your communication skills. Therefore, expect the interviewer to probe your knowledge. Here are a couple of examples of statistics questions you might face in a data science job interview:
“What is linear regression?”
This is a common question that any data scientist should have the answer to. It is a test of both your knowledge and communication skills. When faced with a question like this, keep your answer simple and include examples. For instance:
“Linear regression is how we explain the relationship between two variables. This includes a dependent variable (the possible outcome, dependent on other variables) and an independent variable (one which we can control and manipulate). Plotting data in a graph, linear regression allows us to see the statistical interaction between these two variables, i.e. how they are related. This is a good tool for predictive analysis. For instance, we could use linear regression to analyze the time it would take to build a wall (the independent variable) versus the number of bricklayers required to construct it (the dependent variable).”
Expect follow-up questions, such as the four assumptions of linear regression, or logistic regression.
“What is overfitting?”
Should you get a question asking you to define something, don’t stop with the definition. Take it a step further. Use this kind of data science interview question to show off your knowledge, while applying it to a specific discipline. First, define the concept and describe where you might come across it. Then, if suitable, cover other relevant information:
“Overfitting is when a predictive statistical model is too complex. As a result, it describes random data errors instead of the relationship between variables. It is quite common in machine learning and is sometimes called ‘overtraining’. Here are some ways of preventing it:
- Use cross-validation: Partition the data (or ‘fold’ it), then analyze each fold. Training your algorithm on these folds will tune the model.
- Remove features: One by one, remove each data point from a dataset. This way, you can determine how well your model predicts the removed feature. If it performs poorly, you probably can’t use the model to make general predictions from the complete dataset.
- Stop early: If the latest iteration of an algorithm weakens a model’s ability to predict generalized data, you’ll know that overfitting is a problem. Stopping before this point allows you to avoid overfitting in the first place.”
Remember: statistics questions are good for proving your mathematical and problem-solving skills. How can you best show them off?
3. Programming interview questions
Programming is a fundamental skill for any data scientist. At the very least, you’ll have to show advanced knowledge of Excel and languages like Python, R, and SQL. Questions around programming are designed to test your practical skills. They might include:
“Which programming languages are you most comfortable working with?”
This question gives you a good chance to describe the programming languages you’re familiar with and how you would apply them. You’ll need a good working knowledge of Python (used for structured, object-oriented programming), R (used for statistical computing and graphics, and by data miners for developing statistical software) and SQL (used to create, maintain and manage relational databases). Don’t forget to mention any other tools you use that might be relevant e.g. Jupyter Notebook. Offer examples of how you’ve applied each one in real life.
The interviewer is likely to ask follow-up questions to determine how discerning you are. For example, to test your working knowledge, they might ask whether Python or R is best for text analytics. You might respond:
“Personally, I would choose Python. The Pandas library offers intuitive, pre-existing data tools. These are well-suited to text analytics. In fact, Python generally performs faster for all types of text analytics. I think R is more suited to machine learning.”
This is one example, but it highlights the importance of drawing on details. Use your own experience (such as the libraries you have used) to help your answers stand out.
“Why is data cleaning important?”
Once again, this question is not only probing your knowledge, but your practical skills. First, answer the question by highlighting the importance of data cleaning:
“Although effective data cleaning is time-consuming, it is crucial. After all, a data analyst’s predictions are only as good as the quality of their data.”
Then, talk up your practical skills. Excel, for example:
“Excel is particularly important for cleaning. I often use it to remove corrupt or incorrect data, and to aggregate information from numerous sources into a workable format.”
Show the interviewer you are aware of the features of the software or tool you’re discussing. It can’t hurt to tie it in with key concepts, such as machine learning, to contextualize your knowledge (more on that later).
SQL interview questions for data scientists
SQL (structured query language) questions are very common in data science interviews. They pose a particular challenge because they’re usually based on practice problems. For example, the interviewer might give you a table of data, ask you to extract relevant information from it, order it, and create a report.
SQL tasks generally come in two types. In the first, you’ll have access to a dataset and a computer. The interviewer may watch as you work, asking questions as you go. They won’t expect your work to be perfect, but they will expect you to solve the problem within a reasonable timeframe.
More common (and unfortunately more difficult!) are whiteboard tasks. This is when you’re asked to solve the problem without a computer, showing your work on the board instead. As a result, there’s no computer to flag syntax errors, and so on. You’ll have to think on the spot.
These tasks (though tough) are designed to see how you work under pressure. They’re usually simpler than the problems you’d face in real life. So don’t panic!
4. Data modeling interview questions
While statistics and programming are ‘learned’ skills, data modeling relies on a degree of analytical thinking that’s often harder to grasp. An interviewer will be keen to see if you can demonstrate these skills. What’s more, data modeling is where you’ll add real value to an organization by building a data structure that aligns with their needs. Ultimately, this will turn disparate, raw data into predictive, actionable, and well-visualized insights. Therefore it’s very important to get right. Make sure you familiarize yourself with any modeling techniques you’ve used in the past and have plenty of real-life examples to hand. As ever, keep an eye out for trick questions:
“How is data modeling different from database design?”
A question like this is designed to gauge your skill level. Take the opportunity to talk about what you know:
“In short, data modeling is the first step towards designing a database. It starts with a conceptual model that describes how a complex system should work. This relies on an easy to understand diagram, which shows how data needs to flow. We can then use this model as a blueprint for database design. The design goes a step further, determining in more granular detail where to store what data, how different elements must interrelate, and how it should be output.”
To prepare for data modeling questions, make sure you read up on different types of models, database schemas, and dimensions.
“Describe some common mistakes you’ve encountered during data modeling”
A question like this is not only asking about the mistakes you’ve encountered, but how you solved them. The interviewer wants to know how you identify problems, but also how you fix them. You might reply:
“Common data modeling mistakes I’ve come across include:
- Lack of purpose: Poor clarity on business goals is the most common problem. If I have a vague idea of the solution, my model will be flawed. That’s why I always make sure business goals are clearly defined before I start.
- Building oversized data models: Massive data models are more likely to include mistakes. To get around this, I would simplify the model (for example by restricting myself to no more than 200 tables). Complex detail can come in at the database design stage.
- Improper use of surrogate keys: Identifying data using surrogate keys often overcomplicates things. Instead, where possible, I use natural keys in the data, i.e. unique values, like social security numbers.”
Include solutions in your answers. This will show your initiative and be more likely to impress an employer. You can also apply this technique to any question that focuses on problem issues, such as past projects that didn’t go so well.
5. Machine learning and AI interview questions
Machine learning and AI are important and fast-growing disciplines within data science. Using machine learning, we can now make more accurate, higher-value predictions at a faster pace than ever before—all with minimal human intervention. So prepare for questions that probe your knowledge of this fast-growing field. For instance:
“What is the difference between supervised and unsupervised learning?”
These two concepts are core to machine learning. It can be tempting to focus your preparation on tough technical questions. However, if you struggle to answer simple ones like this, it can be a red flag to an employer. As such, keep your answer uncomplicated. And, as ever, use examples where you can. For instance:
“Supervised learning is where a machine learns using labeled training data. The data uses labeled examples designed to help the machine recognize and categorize. For instance, we might feed the machine pictures of vegetables. Classification labels will tell it which are carrots, which are runner beans, and so on. The ultimate goal is for the machine to predict future output when given new data.
“Unsupervised learning is where the machine infers a function from unlabeled training data. The algorithm will identify data based not on classifications, but by seeking patterns. For instance, ‘vegetables that are orange’ versus ‘vegetables that are green’. Its ultimate goal is not to make sense of the data, but to identify hidden patterns and help us learn more about it.”
The vegetable examples used above are obviously trivial—whenever possible, you should use real-world examples. An interviewer will always ask about past projects at some point, so it’s good practice to get into the habit of weaving examples into your answers. If you’re new to data science and data analytics—don’t worry! You can also talk about what you learned on practice projects.
“How would you gather and clean data before applying machine learning algorithms?”
Above, we covered data cleaning in a programming context. That was to check your practical skills. In the context of machine learning, the question tests your understanding of how concepts apply to specific niches within data science. In short, you must prove that you can link what you know in theory to how it should be used in practice.
To show this, you could cover the following:
- Data profiling: Explain how you would get a deeper understanding of your raw data. For instance, by using different algorithms to explore it.
- Visualizations: Explain how you’d use various graphs (histograms, scatter graphs, etc.) to view data. The aim here is to show that you understand the importance of exploring it from different angles. How you spot patterns between variables, identify potential outliers, and so on.
- General tidying: Finally, discuss how you’d look for syntax errors, e.g. white space, incorrect letter casing, and typos. Touch on how you would deduplicate your data and remove irrelevant values. Demonstrating a systematic approach will highlight both your capabilities and your eye for detail.
Remember: whatever question you’re asked, always bring your answer back to the topic (in this case, machine learning). And if in doubt, ask yourself: What skill is the interviewer looking for?
As a complex, multidisciplinary field, data science interview questions will certainly challenge your knowledge. But as we’ve shown, knowledge is only half the battle. Anyone can rehearse answers. What really matters is showing that you know how to apply your skills in practice. So, to summarize, here are the most common questions you can expect from a data science interview:
- General questions: Use ‘ice-breaking’ questions to your advantage—talk up your personal story, your path, and your passion for data science.
- Statistics questions: Explain complex concepts in simple terms to show off your knowledge and demonstrate your communication skills.
- Programming questions: Be prepared for a practical test of your Excel, Python, R, and SQL skills.
- Data modeling questions: Modeling requires significant analytical thinking. Give the interviewer some insight into the way that you work.
- Machine learning questions: As with any other data science discipline, expect to be asked about machine learning concepts and algorithms.
We’ve said it a few times, but it doesn’t hurt to drive it home once more—use examples! Interviewers will always be more impressed if you can link abstract concepts to real-world experience. Do your homework, embrace your limitations, and always bring the focus back to the job in hand. Most of all, good luck!
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