Cracking the data scientist interview questions is not child’s play. Having the necessary skills and mastery over core concepts of data analysis is critical. Practicing data scientist interview questions is a great way to start your prep.

Working as a data scientist in top tech companies is a dream of many. Moreover, data scientists are also in high demand across the globe as organizations continue to grapple with big data and extract relevant data points.

Having trained over 13,500 professionals, we know what it takes to crack the toughest tech interviews. Our alums consistently land offers from FAANG+ companies. The highest-ever offer received by an IK alum is a whopping **$1.267 Million!**

At IK, you get the unique opportunity to learn from expert instructors who are **hiring managers and tech leads** at Google, Facebook, Apple, and other top Silicon Valley tech companies.

*Want to **nail your next tech interview**? Sign up for our **FREE Webinar.*

In this article, we will look at the sample questions that you may expect during data scientist interviews. We have divided this blog into some of the popular data scientist interview questions, data scientist interview questions for freshers and experienced professionals. We also present some behavioral interview questions for data scientists.

**Most Commonly Asked Data Scientist Interview Questions and Answers**

Here’s a list of frequently asked basic-level questions at data science interviews:

**1. Explain the differences between big data and data science.**

Data science is an interdisciplinary field that looks at analytical aspects of data and involves statistics, data mining, and machine learning principles. Data scientists use these principles to obtain accurate predictions from raw data. Big data works with a large collection of data sets and aims to solve problems pertaining to data management and handling for informed decision-making.

The following table explains the differences between them in detail:

Big Data |
Data Science |

It is the large volume of structured, semi-structured, and unstructured data which is extremely complex to be processed using any of the traditional data-processing tools | It is a multidisciplinary field that emphasizes the use of scientific methods, algorithms, and systems to determine meaningful and actionable insights from data |

It mainly deals in storing, processing, and managing large data sets | The focus is on analyzing the data, building models, and extracting meaningful and actionable insight |

Relies on tools like Hadoop, Spark, NoSQL for storing and processing data | It uses tools such as Python, R, TensorFlow, etc. |

One has to possess knowledge of distributed computing, data storage systems, and data engineering | Skills like statistics, machine learning, data visualization, and data mining are important |

**2. There are missing random values in a data set. How will you deal with it?**

This can be resolved by partitioning the available data into one set with missing values and another with non-missing values.

**3. Define fsck.**

It is an abbreviation for “file system check.” This command can be used for searching for possible errors in the file.

**4. Explain the different techniques used for sampling data.**

There are two major techniques:

- Probability Sampling techniques: Clustered sampling, Simple random sampling, Stratified sampling.
- Non-Probability Sampling techniques: Quota sampling, Convenience sampling, snowball sampling

**5. Describe the different types of deep learning modules.**

The most common frameworks are:

- Pytorch
- Microsoft Cognitive Toolkit
- TensorFlow
- Caffe
- Chainer
- Keras

**6. What is cross-validation?**

Cross-validation is a statistical technique that one can use to improve a model’s performance. This is helpful when the model is dealing with unknown data.

**7. Explain the differences between a test set and a validation set.**

A Test set is used to test and evaluate the trained model's performance. In contrast, a validation set is part of the training set used for selecting different parameters to avoid model overfitting.

Test Set |
Validation Set |

It is used in evaluating the final performance of a trained model on unseen data | It is used for tuning the hyperparameters and selecting the best models during the training |

It is applied only once the training of the model has been completed | It is used during the training of the model for monitoring and improving the model's performance |

Gives an unbiased analysis of the model's generalization to the new data | It helps in adjusting and optimizing the model during the training without overfitting |

**Also read: Career Path to Become a Successful Data Scientist**

**8. Explain the regression data set**

It refers to the data set directory, which contains test data for linear regression. Taking a set of data (xi,yi) to determine the ideal linear relationship is the simplest type of regression.

**9. How will you explain linear regression to a non-tech person?**

Linear Regression refers to a statistical technique that measures the linear relationship between the two variables. Increasing one variable would lead to an increase in the other variable and vice-versa.

**10. Why is data cleansing important?**

Data cleansing allows you to sift through all the data within a database and remove or update information that is incomplete, incorrect, or irrelevant. It is important as it improves the data quality.

Recommended Reading: How to Create an Impressive Data Scientist Resume

**Popular Data Science Interview Questions and Answers at FAANG+ Companies**

Probability and statistics are widely used throughout the career of a data scientist. Therefore, these topics are a crucial part of the interview process for Data Scientists at every company. At FAANG, these topics have a dedicated interview round.

Following are examples of probability and statistics problems that are frequently asked at FAANG+ companies:

### 1. The “choose a door” problem

In the problem, you are on a game show, being asked to choose between three doors. Behind each door, there is either a car or a goat. You choose a door. The host, Monty Hall, picks one of the other doors, which he knows has a goat behind it, and opens it, showing you the goat. (You know, by the rules of the game, that Monty will always reveal a goat.) Monty then asks whether you would like to switch your choice of door to the other remaining door. Assuming you prefer having a car more than having a goat, do you choose to switch or not to switch?

**Switch**- Won’t switch
- Can’t conclude

**Solution:**

Here, we have three possible cases:

If you switch the door, you are more likely to win (i.e., with a 2/3 probability)

### 2. The “fair coin” problem

A coin was flipped 1000 times, and there were 560 heads. For this scenario, develop the hypothesis to test whether the coin is fair or not.

**Solution: **

Let’s assume that the probability of a head in the coin toss is p. We need to test if p is 0.5 or not.

- Null Hypothesis: p = 0.5
- Alternate Hypothesis: p ? 0.5

Using the Central Limit Theorem, we can approximate the total number of heads as normally distributed (since 1000 is a large sample size).

Now, the number of ways of getting x(=560) number of heads in the n(=1000) trial is

This is a binomial distribution.

So, expected number of heads if null hypothesis is true (i.e., p = 0.5) = n*p = 1000*0.5 = 500

Similarly,

Now, since we know that number of heads can be approximated as a normal distribution, we can check how our actual number of heads or sample mean (i.e., 560) is away from the actual mean or population mean (i.e., 500) considering the null hypothesis (p=0.5) is true. We can do that by calculating the z-score:

**z-score = (population mean - sample mean)/standard deviation of the population**

For our case:

99.73% of the normal distribution lies under the 3 standard deviations from the mean. And the z-score is showing that the number is around 3.79 standard deviation away from the mean. Hence, we can say that there is a less than 1% chance that the coin is unbiased, and we **reject the null hypothesis**. Hence, the coin is biased.

**Also read: Data Analyst vs. Data Scientist: Main Difference**

### 3. The elevator problem

Eight people enter an elevator in a building with ten floors. What is the expected number of stoppings?

**Solution:**

There is no assumption about where (specific floor) and when (together or separately) people get on the elevator.

Probability of a person getting off at a specific floor (out of 10) = 1/10

Probability a person not getting off at a specific floor = 1 - 1/10 = 9/10

### 4. The “coin toss” problem

A fair coin is tossed 10 times; given that there were 4 heads in the 10 tosses, what is the probability that the first toss was heads?

**Solution: **

Apply Bayes’ Theorem to solve the problem:

5. Find the distribution of the sum of two random numbers.

You have two** independent, identical,** uniformly distributed random variables x and y **ranging between 0 and 1**. What distribution does the sum of these two random numbers follow? What is the probability that their product is less than 0.5.

**Solution: **Random variable created by the addition of 2 random variables is again a normal random variable.

A quick way to check if the probability of the product of X(0,1) and Y(0,1) is less than 0.5 is to visualize a 2-dimensional plane. All the points (x,y) within the square [0, 1] x [0, 1] fall in the candidate space.

The case when xy = 0.5 makes a curve y = 0.5/x, the area under the curve would represent the cases for which xy <= 0.5. Since the area for the square is 1, that area is the sought probability.

The curve intersects the square at [0.5,1 ] and [1, 0.5].

### 6. Increase the conversion on an e-commerce website

There are a few ideas to increase the conversion on an e-commerce website, such as enabling multiple-items checkout (currently, users can check out one item at a time), allowing non-registered users to checkout, changing the size and color of the “Purchase” button, etc. How do you select which idea to invest in?

**Solution: **

This is an open-ended question based on A/B Testing. It is a vanilla version of the type. The decision of which program to invest in depends on the A/B test results we get from the available options. Please pay close attention to the final goal (improved conversion at checkout), as this also determines the metrics of interest. To answer such questions, usually approach in the following order:

- Identify the metric for tracking
- Explain how to randomize and what your samples are exactly
- Construct null and alternative hypotheses
- Keep the test statistics in mind
- How to draw conclusions from the test statistic computations
- Follow-up analysis

### 7. What are the effects of outliers in linear regression? How to deal with outliers?

**Solution:**

Linear regression is sensitive to outliers. Since linear regression minimizes the sum of squared errors across all observations, when an outlier is present, the fit will change to accommodate. Hence, making the linear regression fit sensitive to outliers.

To deal with outliers, one needs to identify whether the outlier is a valid datapoint or not. If it is due to data collection issues, simply remove the invalid outlier datapoint. If the datapoint is valid, try to understand how common the valid datapoint is. Data transformation and fitting a separate model for the outliers might need to be done for that case.

### 8. How do you decide if a feature is important in a linear regression model?

**Solution:** T-test can be done for the coefficients of the linear regression model, i.e.:

In other words, the T-test will determine whether the jth feature has a statistically significant non-zero coefficient in the model. Generally, a non-zero coefficient feature is considered to be important for the model.

Alternatively, Lasso Regression can be used to identify significant features. The ones with coefficients not sent to zero by the Lasso Regression are considered to be important.

### 9. What can be done if data visualization clearly indicates that the relationship between dependent variable y and independent variable x is not linear?

**Solution: **

### 10. Is R^2 = 1 good, the larger, the better?

**Solution:**

In the following sections, we’ll cover some more sample interview questions asked at FAANG+ companies.

**Also read: Amazon Data Scientist Salary**

### Amazon Data Scientist Interview Questions

Being one of the biggest data-driven companies, Amazon is constantly looking for expert data scientists. If you’re preparing for a data scientist interview at Amazon, the following are some sample questions you can practice:

- Create a Python code that can recognize whether entries to a list have common characters or not.
- Suppose you have an array of integers. You have been asked to find a certain element. What is the algorithm you would use, and what is its efficacy?
- In the case of a long-sorted and short-sorted list, what algorithm would you use to search the long list for the 4 elements?
- Tell us about an instance where you applied machine learning to resolve ambiguous business problems.
- If you have categorical variables and there are thousands of distinct values, how will you encode them?
- Define lstm. How have you used it?
- Enumerate the difference between bagging and boosting.
- How does 1D CNN work?
- Differentiate between linear regression and a t-test?
- How will you locate the customer who has the highest total order cost between 2020-02-02 to 2020-05-06? You can assume that every first name in the dataset is unique.
- Take us through the steps of the cold-start problem in a recommender system?
- Discuss the steps of building a forecasting model.
- How will you create an AB test for a marketing campaign?
- What are Markov chains?
- What is root cause analysis?

Recommended Reading: Amazon Data Scientist Salary

### Facebook Data Scientist Interview Questions

Facebook is one of the major players in data science and offers great job opportunities for data scientists. Following are some sample data scientist interview questions for Facebook interview prep:

- How do you approach any data analytics-based project?
- Explain Gradient Descent
- Why is data cleaning crucial? How do you clean the data?
- Define Autoencoders.
- How will you treat missing values during data analysis?
- How will you optimize the delivery of a million emails?
- What are Artificial Neural Networks?
- Describe the different machine learning models.
- What is the difference between Data Science and Data Analytics?
- How will you ensure good data visualization?

Recommended Reading: Facebook Data Scientist Salary

### Airbnb Data Scientist Interview Questions

Being heavily dependent on tech and data, Airbnb is a great place to work for software engineers and data scientists. You can practice the following interview questions for your data scientist interview at Airbnb.

- If you need to manage a chat thread, which tables and indices do you need in a SQL DB?
- How do you propose to measure the effectiveness of the operations team?
- Explain p-value to a business head.
- Explain the differences between independent and dependent variables.
- What is the goal of A/B Testing?
- Define Prior probability and likelihood?
- Explain the key differences between supervised and unsupervised learning.
- What is the difference between “long” and “wide” format data?
- Explain the utility of a training set.
- What is Logistic Regression?

Recommended Reading: Data Scientist Salary in the United States

**Data Science Interview Questions for Freshers**

If you’re a fresher, here are some data science interview questions that you must prepare for:

- Explain the differences between data analytics and data science.
- Can you describe the various techniques used for data sampling?
- What are the benefits of using data sampling?
- What are precision and recall in data science?
- What is the best way to handle missing values in data?
- Define linear regression. How do you use it in data analysis?
- What is logistic regression, and how is it different from linear regression?
- What are the differences between long and wide-format data?
- List out the differences between supervised learning and unsupervised learning.
- Enlist the various steps involved in an analytics project.
- What do you understand by deep learning?
- What is data cleaning?
- How does traditional application programming vary from data science?
- What are the differences between Normalization and Standardization?
- Define tensors in data science.

Recommended Reading: Data Engineer vs. Data Scientist — Everything You Need to Know

**Data Science Interview Questions for Experienced Candidates**

Experienced candidates applying for data scientist roles at tech companies can expect the following types of interview questions:

- How do you handle unbalanced binary classification?
- Discuss three types of machine learning algorithms.
- What is a random forest algorithm?
- Define Cross-Validation.
- What is bias?
- What is the CART algorithm for decision trees?
- Describe the different nodes of a decision tree.
- Have you used hypothesis testing in machine learning problems?
- What is ANOVA testing?
- In the case of imbalance classification, how will you calculate F-measure and precision?
- Explain gradient descent with respect to linear models.
- Why should you use regularization? What are the differences between L1 and L2 regularization?
- Describe the differences between a box plot and a histogram.
- What is a confusion matrix?
- Describe outlier value. How do you treat them?

**More Sample Questions for Data Science Technical Interviews**

Here are a few more technical interview questions for practicing for your data scientist interview:

- What do you mean by cluster sampling and systematic sampling?
- Describe the differences between true-positive rate and false-positive rate.
- What is Naive Bayes? Why is it known as Naive?
- What do you understand about the “curse of dimensionality”?
- What is cross-validation in data science?
- What do you know about cross-validation?
- How can you select an ideal value of K for K-means clustering?
- What are the steps of building a random forest model?
- What is ensemble learning?
- How will you define clusters in cluster algorithm?

Recommended Reading: 7 Best Data Science Books for Interview Preparation

**Behavioral Interview Questions for Data Scientists**

While there will be a heavy focus on your data science knowledge and skills, data scientist interviews also include behavioral rounds. Following are some behavioral interview questions you can practice to ace your data scientist interview:

- Describe a time when you used data for presenting data-driven statistics.
- Do you think vacations are important? How often do you think one should take a vacation?
- Did you ever have two deadlines that you had to meet simultaneously? How did you manage that?
- Describe a time when you had a disagreement with a senior over a project. How did you handle it?
- How will you handle the situation if you have an insubordinate team member?
- Why do you want to work as a data scientist with this company?
- Which is your favorite leadership principle?
- How do you ensure high productivity levels at work?
- Have you ever had to explain a technical concept to a non-technical person? Was it difficult to do so?
- How do you prioritize your work?

Recommended Reading: Python Data Science Interview Questions

That concludes the comprehensive list of data scientist interview questions. Make sure you practice these frequently asked questions to prepare yourself for the interview.

## How to Crack Data Scientist Interview Questions

If you need help with your prep, join** Interview Kickstart’s ****Data Science Interview Course**** — **the first-of-its-kind, domain-specific tech interview prep program designed and taught by FAANG+ instructors.

IK is the gold standard in tech interview prep. Our programs include a comprehensive curriculum, unmatched teaching methods, FAANG+ instructors, and career coaching to help you nail your next tech interview.

Sign up for our FREE webinar to uplevel your career!

## FAQs on Data Scientist Interview Questions

### 1. What type of questions are asked in a data scientist interview?

Data science interview questions are usually based on statistics, coding, probability, quantitative aptitude, and data science fundamentals.

### 2. Are coding questions asked at data scientist interviews?

Yes. In addition to core data science questions, you can also expect easy to medium Leetcode problems or Python-based data manipulation problems. Your knowledge of SQL will also be tested through coding questions.

### 3. Are behavioral questions asked at data scientist interviews?

Yes. Behavioral questions help hiring managers understand if you are a good fit for the role and company culture. You can expect a few behavioral questions during the data scientist interview.

### 4. What topics should I prepare to answer data scientist interview questions?

Some domain-specific topics that you must prepare include SQL, probability and statistics, distributions, hypothesis testing, p-value, statistical significance, A/B testing, causal impact and inference, and metrics. These will prepare you for data scientist interview questions.

### 5. Is having a master’s degree essential to work as a Data Scientist at FAANG?

Based on our research, you can work as a data scientist even though you only have a bachelor’s degree. You can always upgrade your skills via a data science boot camp. But for better career prospects, having an advanced degree may be useful.