Uploaded May 2021 | Updated September 2026, 1 hour ago
In this video, we’ll compare data science interview questions between FAANG and non-FAANG companies to uncover the differences. This is a continuation of our previous video 'Analysis of 900 Data Science Interview Questions' (youtu.be/giGIPINLqJs) where we analyzed 900 data science interview questions collected from various sites over the last few years. Now let’s take the same 900 questions and split them into FAANG and non-FAANG companies.
Link to the questions:
Question by Facebook (Investigate the Discrepancy): platform.stratascratch.com/technical/2203-investigate-the-discrepancy?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Facebook (Users Activity Per Month Day): platform.stratascratch.com/coding/2006-users-activity-per-month-day?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Google (Common Friends Friend): platform.stratascratch.com/coding/9821-common-friends-friend?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Google (Highly Correlated Predictors): platform.stratascratch.com/technical/2017-highly-correlated-predictors?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Amazon (Neural Network and Logistic Regression): platform.stratascratch.com/technical/2212-neural-network-and-logistic-regression?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Amazon (Random Bucketing): platform.stratascratch.com/technical/2055-random-bucketing?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Apple (Users in Meaningful Segments): platform.stratascratch.com/technical/2041-users-in-meaningful-segments?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Link to the complete blog: stratascratch.com/blog/data-science-interview-guide-questions-from-80-different-companies?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: bit.ly/3jifw81
👉 Playlist for data science interview tips: bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
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Timeline:
Intro: (0:00)
FAANG vs Non-FAANG: (1:08)
Facebook: (3:30)
Google: (5:40)
Amazon: (7:17)
Apple: (9:01)
Conclusion: (10:45)
______________________________________________________________________
If you want interview practice with real data science interview questions, visit platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
______________________________________________________________________
#DataScience #DataScienceInterviewQuestions
In this video, we’ll compare data science interview questions between FAANG and non-FAANG companies to uncover the differences. This is a continuation of our previous video 'Analysis of 900 Data Science Interview Questions' (youtu.be/giGIPINLqJs) where we analyzed 900 data science interview questions collected from various sites over the last few years. Now let’s take the same 900 questions and split them into FAANG and non-FAANG companies.
Link to the questions:
Question by Facebook (Investigate the Discrepancy): platform.stratascratch.com/technical/2203-investigate-the-discrepancy?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Facebook (Users Activity Per Month Day): platform.stratascratch.com/coding/2006-users-activity-per-month-day?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Google (Common Friends Friend): platform.stratascratch.com/coding/9821-common-friends-friend?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Google (Highly Correlated Predictors): platform.stratascratch.com/technical/2017-highly-correlated-predictors?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Amazon (Neural Network and Logistic Regression): platform.stratascratch.com/technical/2212-neural-network-and-logistic-regression?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Amazon (Random Bucketing): platform.stratascratch.com/technical/2055-random-bucketing?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Question by Apple (Users in Meaningful Segments): platform.stratascratch.com/technical/2041-users-in-meaningful-segments?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Link to the complete blog: stratascratch.com/blog/data-science-interview-guide-questions-from-80-different-companies?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: bit.ly/3jifw81
👉 Playlist for data science interview tips: bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
Timeline:
Intro: (0:00)
FAANG vs Non-FAANG: (1:08)
Facebook: (3:30)
Google: (5:40)
Amazon: (7:17)
Apple: (9:01)
Conclusion: (10:45)
______________________________________________________________________
If you want interview practice with real data science interview questions, visit platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
______________________________________________________________________
#DataScience #DataScienceInterviewQuestions
![Data Preparation for Modeling [DoorDash Data Science Project]](https://i.ytimg.com/vi/Sf6jn8QZHhc/mqdefault.jpg "Data Preparation for Modeling [DoorDash Data Science Project]
This video will walk you through the DoorDash Delivery Duration Prediction data project. Well cover all the stages of the preparation of data for modeling.
🧑💻 Go to the project through the link below and follow along with me: https://platform.stratascratch.com/data-projects/delivery-duration-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Watch the next parts:
📌 Part 2: Collinearity and Removing Redundancies: https://youtu.be/m3zEV10qvE8
📌 Part 3: Multicollinearity and Feature Selection: https://youtu.be/gh5JzALBQvU
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Timeline:
Intro: (0:00)
Take-home assignment from DoorDash: (0:10 )
Exploring and understanding the data: (0:47)
Coding the solution: (2:05)
Feature Creation (3:10)
Data Preparation for Modeling (5:34)
Conclusion: (10:16)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#StrataScratch #DoordashDataProject #DataModeling")
![Most Common Data Science SQL Interview Question from DoorDash [window functions & partitions]](https://i.ytimg.com/vi/T1UhSuKqy3A/mqdefault.jpg "Most Common Data Science SQL Interview Question from DoorDash [window functions & partitions]
This is the most common data science SQL interview question from DoorDash, a food delivery company. This question tests your ability to split your data into percentiles using window functions. I’ll walk you through solving the question like we’re in an interview and give you some tips on how to approach the solution.
Go to the question through the link below and follow along with me.
Link to the question: https://platform.stratascratch.com/coding/2036-lowest-revenue-generated-restaurants?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Timeline:
Intro: (0:00)
Interview Question: (0:35)
Framework to solve the problem: (1:40)
Understand your data: (3:58)
Formulate your approach: (6:40)
Code Execution: (8:55)
Code Optimization: (14:35)
Conclusion: (17:08)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#DataScience #SQLInterviewQuestion")
