Uploaded August 2022 | Updated September 2026, 1 hour ago
In this video, we'll take a close look at python window functions, more specifically the aggregate functions: Group By, Rolling, and Expanding. We’ll go over each aggregate function, along with implementing the functions in different interview questions.
Link to the question:
platform.stratascratch.com/coding/9899-percentage-of-total-spend?code_type=2&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Link to the article: stratascratch.com/blog/python-window-functions/?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
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👉 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)
Python Window Functions Introduction: (00:16)
Aggregate Functions: Group By: (01:43)
Amazon Interview Question: (02:20)
Solution: Formulate Approach: (2:58)
Solution: Breakdown: (04:30)
Solution: Code Execution (04:25)
Edge Case Consideration (05:55)
Edge Case Implementation (06:31)
Aggregate Functions: Rolling And Expanding (6:53)
San Francisco Weather Question: Rolling Aggregate Function: (09:35)
Solution: Formulate Approach: (09:50)
Solution: Code Execution (10:13)
San Francisco Weather Question: Expanding Aggregate Function: (14:00)
Solution: Formulate Approach: (14:10)
Solution: Code Execution (14:24)
Conclusion: (15:35)
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About The Platform:
I'm using StrataScratch (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 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
______________________________________________________________________
In this video, we'll take a close look at python window functions, more specifically the aggregate functions: Group By, Rolling, and Expanding. We’ll go over each aggregate function, along with implementing the functions in different interview questions.
Link to the question:
platform.stratascratch.com/coding/9899-percentage-of-total-spend?code_type=2&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Link to the article: stratascratch.com/blog/python-window-functions/?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)
Python Window Functions Introduction: (00:16)
Aggregate Functions: Group By: (01:43)
Amazon Interview Question: (02:20)
Solution: Formulate Approach: (2:58)
Solution: Breakdown: (04:30)
Solution: Code Execution (04:25)
Edge Case Consideration (05:55)
Edge Case Implementation (06:31)
Aggregate Functions: Rolling And Expanding (6:53)
San Francisco Weather Question: Rolling Aggregate Function: (09:35)
Solution: Formulate Approach: (09:50)
Solution: Code Execution (10:13)
San Francisco Weather Question: Expanding Aggregate Function: (14:00)
Solution: Formulate Approach: (14:10)
Solution: Code Execution (14:24)
Conclusion: (15:35)
______________________________________________________________________
About The Platform:
I'm using StrataScratch (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 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
______________________________________________________________________
![How To Handle Multicollinearity and Feature Selection [DoorDash Data Science Project]](https://i.ytimg.com/vi/gh5JzALBQvU/mqdefault.jpg "How To Handle Multicollinearity and Feature Selection [DoorDash Data Science Project]
In this video, well give you a brief introduction to multicollinearity and feature selection, and show you how to solve the problem using a variety of methods. Well be using the DoorDash data science project to demonstrate how to apply these concepts in practice. In addition, well use the Random Forest regression method to determine which features are most important for predicting delivery times.
Watch our previous videos:
📌 Part 1: Data Preparation for Modeling: https://youtu.be/Sf6jn8QZHhc
📌 Part 2: Collinearity and Removing Redundancies: https://youtu.be/m3zEV10qvE8
🧑💻 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=multicollinearity+%26+feature+selection
👉 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)
Quick recap: (0:25)
Removing multicollinearity: (0:48)
Feature selection (2:56)
Conclusion: (8:17)
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 #Python")
![Data Science SQL Interview Question Walkthrough [Microsoft] - Window Function: Ranking](https://i.ytimg.com/vi/i-E4pdU2qXM/mqdefault.jpg "Data Science SQL Interview Question Walkthrough [Microsoft] - Window Function: Ranking
This Data Science SQL interview question is from Microsoft, and tests your ability to write window functions to rank data. 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/2026-bottom-2-companies-by-mobile-usage?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: (0:31)
Framework to solve the problem: (1:25)
Understand your data: (2:33)
Formulate your approach: (5:05)
Code Execution: (7:09)
Code Optimization: (15:54)
Conclusion: (18:10)
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
#MicrosoftDataScienceInterview")
