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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.

But cracking these interviews 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.

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In this article, we will look at the sample questions that you may expect during data scientist interviews. Here’s what we will cover in this guide:

- Most commonly asked data scientist interview questions and answers
- Data scientist interview questions for freshers
- Data scientist interview questions for experienced candidates
- Amazon data scientist interview questions
- Facebook data scientist interview questions
- Airbnb data scientist interview questions
- Data scientist technical interview questions
- Data scientist behavioral interview questions
- FAQs about data scientist interview questions

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

Data science is an interdisciplinary field that looks at analytical aspects of data and involves statistics, data mining, 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.

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

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

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

The most common frameworks are:

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

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.

A Test set is used for testing and evaluating the performance of the trained model. In contrast, a validation set is part of the training set used for selecting different parameters for avoiding model overfitting.

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.

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.

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

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

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 tree?
- 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 difference between a box plot and a histogram.
- What is a confusion matrix?
- Describe outlier value. How do you treat them?

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 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

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

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

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.

**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.

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. **Click here** to learn more about the program.

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