The Complete Guide to Acing Your Data Science Interview

It is changing how businesses understand and use their data as data science is a field that is growing quickly. Because of this, businesses are looking for data scientists more and more to help them make sense of their data and get business results. This has led to a high demand for data scientists, and competition for these positions can be fierce. Below is a list of the 100 most common data science interview questions that we think you will be asked. This will help you prepare for your interview.

There is a short explanation of the main ideas and skills that each interview question tests, along with advice on how to approach and answer the question. This list of data science interview questions will help you get ready for your next interview by getting to know them and practicing your answers.

Interviewing for a data science role can be intimidating. You need to demonstrate proficiency in statistics, coding, machine learning, and communication skills Preparing for data science interviews takes time and dedication However, with the right strategy, you can master the art of acing your data science interview.

I’ll show you everything you need to know to ace your next data science interview in this complete guide. As part of my job, I’ve talked to a lot of data scientists, so I’m going to share my best tips with you.

Overview of the Data Science Interview Process

Data science interviews typically consist of 4-5 rounds

Screening Round: This is a short 30-minute call with the recruiter to talk about your background and why you want the job. Be prepared to walk through your resume and any portfolio projects.
Technical Screen: Expect coding challenges and statistical questions to assess your technical abilities. Brush up on probability, stats, Python, R, and SQL.
Take Home Assignment: You will be given a sample dataset and asked to work through the data science lifecycle – data cleaning, EDA, modeling, evaluation, and visualization.
Onsite Interview: Here you will meet with various members of the data science team. Expect Deep technical questions on statistics and modeling as well as product sense questions.
Executive Interview: The final round is usually with a senior leader or executive. They will be assessing your communication skills, strategic thinking, and leadership potential.

Now let’s dive into the specifics of how to prepare for each stage.

Screening Interview Tips

The screening call is your first impression, so you want to nail it! Here are my top tips:

Research the company: Understand their business, products, users, and competitors. This shows your interest in the role.
Review the job description: Identify the required and preferred skills. Be ready to speak to how your background aligns.
Highlight relevant projects: Choose portfolio projects that match the role. Explain the business impact you drove.
Prepare your elevator pitch: Craft a 1-2 minute summary about your background, experience, and why you are passionate about data science.
Have questions ready: Ask thoughtful questions that show your understanding of the company and role. This is an evaluation process for both sides.

With upfront preparation, you can confidently sail through the initial screening call.

Technical Screening Questions

After the recruiter screen, you will usually face a technical screen with an engineer or data scientist. This assesses your hands-on abilities in statistics, coding, and modeling.

Here are some examples of common technical screening questions:

Stats and Probability

Explain p-values and statistical significance.
How is a Gaussian distribution different from Poisson distribution?
What is selection bias and how can you avoid it?

Python and R Coding

Print the first 10 fibonacci numbers using Python.
Read in a CSV file and find the median value of each column in Pandas.
How do you handle missing values in an R dataframe?

SQL Queries

Write a query to find the 5 highest paid employees.
Join two tables and return records where the ids match in both.
Calculate the total sales per product per month.

Machine Learning Concepts

Explain overfitting and techniques to avoid it.
When would you choose random forest over linear regression?
What is cross-validation and why is it used?

I recommend creating flashcards on Python, stats, SQL, and machine learning fundamentals to drill leading up to your interview. Having this core knowledge solidified will help you successfully pass the technical screening round.

Take Home Assignment

Many companies are replacing whiteboard algorithm questions with take home assignments. This is your chance to flaunt your data science skills!

You will be provided a sample dataset and business problem to work through. Timeframes range from 2-7 days depending on the company.

Here are my tips for acing your take home project:

Ask clarifying questions upfront: This ensures you understand the goals and available data.
Document your approach: Explain your methodology and thought process. This is as important as the results!
Include data cleaning and EDA: Don’t jump straight to modeling. Showcase your abilities to handle messy data.
Try multiple models: Demonstrate your knowledge of different algorithms and when to apply them.
Summarize key findings: Interpret your results and make data-driven recommendations. Communicate insights clearly.
Make it reproducible: Modularize your code and include instructions so others can replicate your work.

With a structured, methodical approach you can produce an impressive take home project that gets the green light to the onsite interview.

Onsite Interview Prep

Congratulations, you made it to the last round! The onsite interview will be 4-6 hours at the company office with various data science leaders and cross-functional partners like engineers and product managers.

Here is what to expect and how to prepare:

Technical Questions

You will face another round of rigorous statistics, modeling, and coding questions. Many will involve analyzing hypothetical scenarios.

Review key algorithms: Know when to apply linear regression vs logistic regression vs random forest. Understand regularization techniques.
Practice model evaluation: Be ready to discuss overfitting, underfitting, bias-variance tradeoff, precision, recall, AUC, etc.
Prepare for system design: You may be asked to design a machine learning production environment. Understand concepts like ETL, model monitoring, and retraining.

I recommend working through case studies on datasets like the Titanic survival or wine classification. This will sharpen your technical skills for modeling real world scenarios.

Product Sense Questions

Evaluators will also assess your product intuition and ability to apply data science to drive business impact.

Some examples of product sense questions:

How would you A/B test a new recommendation algorithm on our platform?
Our clickthrough rate has dropped 20% month-over-month. What data would you examine to diagnose the issue?
How could we build a churn prediction model for our service? What metrics would define churn?
We want to launch personalized push notifications to users. How should we determine notification frequency to maximize engagement but minimize spamminess?

The key here is to develop an intuitive sense for how data science can guide product decisions. Study your potential employer’s product and business model thoroughly to prepare.

Behavioral & Communication

Your soft skills are just as crucial as technical abilities. Interviewers will probe your communication style, emotional intelligence, collaboration abilities, and culture fit.

Some behavioral questions to prep for:

Tell me about a time you convinced a cross-functional partner to implement your idea.
How do you balance speed vs accuracy when making data-driven decisions under deadlines?
Describe a mistake you made and what you learned from it.
When dealing with a disagreement on your team, how did you seek to understand an opposing viewpoint?
What would your manager say is an area you need to improve?

I recommend the STAR method (Situation, Task, Action, Result) to structure your answers. Provide real examples that show self-awareness and a team-oriented mindset.

Executive Interview

Finally, you will likely meet with a senior executive to evaluate your leadership potential and strategic thinking. Come prepared with smart questions that show your understanding of their challenges and how data science can transform the business.

Some examples:

What are the biggest growth opportunities you see for the company in the next 5 years and how could data science contribute?
What difficulties have you faced in scaling data science capabilities? How have you sought to overcome those?
What are the key KPIs you measure your data science team against? How would you know if they were succeeding or failing?

The executive interview is your chance to demonstrate strategic acumen and leadership potential. Inspire them with your vision to take their data science impact to the next level!

Data Science Interview Mistakes to Avoid

I’ve interviewed countless data scientists over my career. Avoid these common mistakes candidates make:

Not having basic stats, coding, and ML concepts solidified
Jumping straight into modeling without data cleaning and EDA
Presenting results without interpreting them in a business context
Poor communication skills – both technical and non-technical
Arrogance or unwillingness to admit when unsure of something
Lack of flexibility in exploring different approaches to a problem

With thorough preparation across the technical and non-technical aspects of data science, you can avoid these pitfalls.

The interview process is grueling, but stick with it! Getting your first data science role is the hardest. Once you land that first job and build experience, future interviews will be a breeze.

data science interview questions

Intermediate Interview Questions on Statistics for Data Science

A. The Central Limit Theorem is one of the most important ideas in statistics. It says that as the sample size grows, the sample mean’s distribution will become more like a normal distribution. This is true regardless of the underlying distribution of the population from which the sample is drawn. This means that we can use methods based on normal distribution to draw conclusions about the population even if some of the data points in a sample are not normally distributed. All we have to do is take the average of a large enough number of them.

A. The two kinds of target variables are:

Number-based or continuous variables: Their values are in a range, and they can be any number between that range and the time of the prediction. Values don’t have to be from the same range, though.

For example: Height of students – 5; 5.1; 6; 6.7; 7; 4.5; 5.11

Here the range of the values is (4,7)

And, the height of some new students can/cannot be any value from this range.

You can only put a categorical variable into one of a small, usually fixed number of possible values. This means that each person or thing you observe fits into a certain group based on a qualitative property.

This type of categorical variable can only have two possible values. It is also known as a dichotomous variable. Categorical variables with more than two possible values are called polytomous variables.

For example Exam Result: Pass, Fail (Binary categorical variable)

The blood type of a person: A, B, O, AB (polytomous categorical variable)

A. The mean, median, and mode of a dataset will all be the same if and only if the dataset is made up of a single value that happens 10% of the time.

For example, consider the following dataset: 3, 3, 3, 3, 3, 3. The mean of this dataset is 3, the median is 3, and the mode is 3. This is because the dataset consists of a single value (3) that occurs with 100% frequency.

If, on the other hand, the dataset has more than one value, the mean, median, and mode will usually be different. For example, consider the following dataset: 1, 2, 3, 4, 5. The mean of this dataset is 3, the median is 3, and the mode is 1. This is because the dataset contains multiple values, and no value occurs with 100% frequency.

It is important to keep in mind that outliers, or very high or low values in the dataset, can change the mean, median, and mode. If the dataset has some very high or very low values, the mode may be very different from the mean and median. This is also true if the dataset only has one very high or very low value.

A. In statistics, variance, and bias are two measures of the quality or accuracy of a model or estimator.

Variance: Variance measures the amount of spread or dispersion in a dataset. It is calculated as the average squared deviation from the mean. If the variance is high, the data are spread out and may be more likely to be wrong. If the variance is low, the data are close to the mean and may be more accurate.

There is a difference between what an estimator is supposed to do and what it actually does. This is called bias. As the bias goes up, the estimator is more likely to be close to the true value; a low bias means that the estimator is more likely to be off.

It is important to consider both variance and bias when evaluating the quality of a model or estimator. When there is low bias and high variance in a model, it may overfit. When there is high bias and low variance in a model, it may underfit. Finding the right balance between bias and variance is an important aspect of model selection and optimization.

A. Two types of errors can occur in hypothesis testing: Type I errors and Type II errors.

When the null hypothesis is true but is thrown out, this is called a Type I error, also called a “false positive.” This kind of mistake is shown by the Greek letter alpha (α), and its level is usually set to 0. 05. This means that there is a 5% chance of making a Type I error or a false positive.

It is a Type II error, also called a “false negative,” when the null hypothesis is wrong but not thrown out. The Greek letter beta (²) stands for this kind of mistake, which is often shown as 1 – ², where ² is the test’s power. The power of the test is the probability of correctly rejecting the null hypothesis when it is false.

It’s important to try to minimize the chances of both types of errors in hypothesis testing.

A. In the event that we do the experiment again, the confidence interval tells us what range of results we can expect. It is the mean of the result plus and minus the expected variation.

The standard error of the estimate tells us what that is, and the mean of the estimate is in the middle of the interval. The most common confidence interval is 95%.

A. Correlation is a statistical measure that describes the strength and direction of a linear relationship between two variables. A positive correlation means that both variables go up or down at the same time. A negative correlation means that both variables go up or down at different times. Covariance is a measure of the joint variability of two random variables. It is used to measure how two variables are related.

Advanced Python Interview Questions

A. In Python, a lambda function is a small anonymous function. You can use lambda functions when you don’t want to define a function using the def keyword.

Lambda functions are useful when you need a small function for a short period of time. They are often used in combination with higher-order functions, such as map(), filter(), and reduce().

Here’s an example of a lambda function in Python:

x = lambda a : a + 10

In this example, the lambda function takes one argument (a) and adds 10 to it. The lambda function returns the result of this operation when it is called.

Lambda functions are important because they allow you to create small anonymous functions in a concise way. They are often used in functional programming, a programming paradigm that emphasizes using functions to solve problems.

A. In Python, the assert statement is used to test a condition. If the condition is True, then the program continues to execute. If the condition is False, then the program raises an AssertionError exception.

The assert statement is often used to check the internal consistency of a program. For instance, you could use an assert statement to make sure that a list is in the right order before running a binary search on it.

It is important to remember that the assert statement is only meant to be used for debugging and not to handle runtime errors. For real-world code, you should use try and except blocks to handle any errors that come up during runtime.

A. Decorators let you change or add to the features of a Python function, method, or class without having to change their source code. Decorators are usually made up of functions that take another function as an argument and give back a new function with the right behavior.

There is a special function called a decorator that goes right before the function, method, or class it decorates. It starts with the @ symbol. The @ symbol is used to indicate that the following function is a decorator.

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FAQ

What questions are asked for a data science interview?

10 Most Asked Data Science Interview Questions What are the differences between supervised and unsupervised learning? Explain the steps in making a decision tree. Differentiate between univariate, bivariate, and multivariate analysis. How should you maintain a deployed model?

What are the 4 types of data science?

In data analytics and data science, there are four main types of data analysis: Descriptive, diagnostic, predictive, and prescriptive. In this post, we’ll explain each of the four and consider why they’re useful.

Are data scientist interviews hard?

Are data science interviews hard? Yes. To pass a data science interview, you have to demonstrate proficiency in multiple areas such as statistics & probability, coding, data analysis, machine learning, product sense, and reporting.