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Top Facebook Data Scientist Interview Questions and Answers

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The most commonly asked Facebook data scientist interview questions will help you prepare for your upcoming interview at this tech mammoth.

Facebook is one of the Tier-1 tech giants in the world. Every software geek has a dream of working at this organization. Although the interview process isn’t easy to crack, we have narrowed it down with some sample Facebook data scientist interview questions.

Keep scrolling to get more details about Facebook data scientist interview questions.

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In this article, we will cover:

  • Common Facebook Data Scientist Interview Questions and Answers
  • Facebook Data Scientist Interview Questions on Data Analytics
  • Sample Facebook Data Scientist Interview Questions for Practice
  • FAQs on Facebook Data Scientist Interview Questions

Going through some of the sample Facebook data scientist interview questions before your interview will help you understand the interview process better.

Common Facebook Data Scientist Interview Questions and Answers

This section discusses the most commonly asked Facebook interview questions for data scientists:

Q1. How to perform Logistic Regression?

Logistic regression measures the relationship between a dependent variable and one or more independent variables by calculating the probability using its sigmoid (logistic function).

Q2. Explain the steps to make a decision tree.

Steps to create a decision tree-

  • Take an entire data set as an input.
  • Calculate the entropy of the target variable along with the predictor attributes.
  • The information gain of all attributes is then calculated.
  • Choose an attribute with the highest information gain as the root node.
  • The same process is then repeated on every branch until the decision node of each branch is complete.

Q3. What is the process of building a random forest model?

A random forest model is created by following the steps:

  • First, select "k" features from a total of "m" features where k << m.
  • Second, calculate the node D among the "k" features using the best split point.
  • Third, split the nodes into daughter nodes using the best split.
  • Repeat the second and the third steps until the leaf nodes are finalized.
  • Now build a forest by repeating the steps from first to fourth for "n" times to create an "n" number of trees.

Q4. Differentiate between univariate, bivariate, and multivariate analysis.

  • Univariate contains one variable. Its purpose is to describe the data and find patterns that exist within it.
  • Bivariate contains two variables. The analysis deals with causes and relationships.
  • Multivariate involves three or more variables. It is similar to a bivariate but contains more than one dependent variable.

Q5. How will you calculate the Euclidean distance in Python for the given points?
plot1 = [1,3] plot2 = [2,5]

The Euclidean distance can be solved as follows:

euclidean_distance = sqrt( (plot1[0]-plot2[0])**2 + (plot1[1]-plot2[1])**2 )

Additional Facebook Data Scientist Interview Questions

  1. How to avoid overfitting the model?
  2. What are the feature selection methods used to select the right variables?
  3. What are dimensionality reduction and its benefits?
  4. Find the popularity percentage for each user on Facebook.
  5. What Facebook product is your favorite? How would you like to improve it?
  6. What KPIs would you use to measure the success of the newsfeed?
  7. Which Facebook products are you familiar with?
  8. How would you judge the success of any product?
  9. When the activity in the Facebook group is down by 20%, what would you do?
  10. Which functionalities are involved in creating reactions on Facebook?

These questions might be helpful for software developers looking to change their career paths and become Facebook data scientists.

Recommended Reading: Microsoft Data Science Interview Questions

Facebook Data Scientist Interview Questions with Answer on Data Analytics 

If you are looking for Facebook data scientist interview questions on Data Analytics, these are some of the sample questions and answers that’ll help you prepare better. These questions are applicable for both beginners as well as experienced candidates.

Q1. What is the data analysis process?

Data analysis refers to the process of assembling, cleaning, interpreting, transforming, and modeling data to get information. It helps generate reports to help businesses become more profitable.

Q2. What are the challenges Data Analysts face?

Data Analysts face challenges like - 

  • Duplicate entries and spelling errors.
  • Data representation is obtained from multiple sources; thus, it may differ and create a challenge for analysts.
  • Incomplete data is one of the major challenges faced by a data analyst.
  • Sometimes data is extracted from a poor source; hence it takes more time to clean.
  • Data blending from multiple sources is a difficult task.
  • Insufficient data architecture and tools.

Q3. Define Data cleansing.

Data Cleansing is the process of identifying and then modifying, replacing, or deleting incorrect or missing parts of the data as required. The purpose of this element is to ensure the data is correct, usable, and consistent.

Q4. List the tools used for data analysis.

Some of the common tools used for data analysis are - 

  • RapidMiner
  • Google Fusion Tables
  • Google Search Operators
  • Solver
  • Wolfram Alpha
  • NodeXL
  • OpenRefine
  • Tableau
  • io

Q5. Which validation methods do Data Analysts apply?

The process of data validation requires accurate information generation along with a good quality product. So, there are four data validation methods commonly used by Data Analysts.

  • First-Level Validation
  • Form-Level Validation
  • Data Saving Validation
  • Search Criteria Validation

Additional Facebook Data Scientist Interview Questions on Data Analytics

  1. Differentiate between Data Mining and Data Profiling.
  2. What is an Outlier?
  3. What are the ways to detect an Outlier?
  4. How is Data Analysis different from Data Mining?
  5. What is Normal Distribution?
  6. Explain the KNN imputation method.
  7. How does data visualization help?
  8. Explain a Hash table.
  9. Mention some of the python libraries used in Data Analysis.
  10. Write the characteristics of a good data model.

Read Python Data Science Interview Questions to learn about the common Python-based interview questions.

Sample Facebook Data Scientist Interview Questions for Practice

Here are a few sample Facebook data scientist interview questions essential for your tech interview:

  1. Calculate the total revenue from each Facebook customer in March 2019.
  2. How to find the popularity percentage for each user on Facebook?
  3. What are the expectations of the variance?
  4. You are given data on Facebook users friending and unfriending each other. How will you find whether a given pair of members are currently friends?
  5. Explain the probability of pulling a different color or shape card from a deck of 52 cards.
  6. What is the process of mapping nicknames used by Facebook users to their real names?
  7. How to find the number of “likes” that are increasing by building a “like” score based on its propensities?
  8. What is the process of finding the date with the highest total energy consumption from the Facebook data centers?
  9. How would you investigate why, after the launch of a new notification system, the acceptance rate of friends drops by 15%?
  10. What is the process of predicting churn rate?

Working with Facebook is a once-in-a-lifetime opportunity for tech enthusiasts. On the other hand, the interview is based on your knowledge and work experience. Prepare for the Facebook data scientist interview questions to broaden your knowledge and increase your chances of acceptance at this tech behemoth.

For more data science interview questions, read this article.

FAQs on Facebook Data Scientist Interview Questions

Q1. How to prepare for Facebook data scientist interview questions?

Follow these steps to prepare for a Facebook interview: practice mock interviews, solve SQL problems daily, keep practicing under strict time, be thorough with data structures, algorithms, and complexity.

Q2. What skills does Facebook look for in its employees?

Facebook looks for employees who are bold, impactful, fast-paced, open-minded, and always ready to create social value.

Q3. How long is the Facebook interview process?

The Facebook interview process takes four to eight weeks to complete in most cases. There are a total of seven interview rounds in the entire process.

Q4. What are the programming languages you must know for the interview at Facebook?

Candidates must learn C, C++, Java, and Python if applying to Facebook.

Q5. How much does a data scientist at Facebook make?

At the entry level, a Facebook data scientist’s salary is around $153,046. In addition, it includes compensation in cash and stock bonuses ranging from $10,000- $35,000.

Gear Up for Your Next Data Science Interview

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