Uploaded August 2022 | Updated September 2026, 4 hours ago
This video shows you how to use the STRING_AGG function during technical data science interviews. Despite its relatively uncommon use, STRING_AGG is a useful SQL aggregation function when solving data science interview problems. An example of this is the recent and very tasty interview question from Amazon that deals with some cookbook titles.
Link to the question: platform.stratascratch.com/coding/2089-cookbook-recipes?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Link to the article: stratascratch.com/blog/how-to-use-string_agg-to-solve-sql-questions-in-data-science-interviews/?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)
The string_agg() function: (0:17)
Interview Question: (4:00)
Formulate your approach: (5:37)
Code Execution: (7:40)
Conclusion: (21:26)
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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
______________________________________________________________________
This video shows you how to use the STRING_AGG function during technical data science interviews. Despite its relatively uncommon use, STRING_AGG is a useful SQL aggregation function when solving data science interview problems. An example of this is the recent and very tasty interview question from Amazon that deals with some cookbook titles.
Link to the question: platform.stratascratch.com/coding/2089-cookbook-recipes?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Link to the article: stratascratch.com/blog/how-to-use-string_agg-to-solve-sql-questions-in-data-science-interviews/?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)
The string_agg() function: (0:17)
Interview Question: (4:00)
Formulate your approach: (5:37)
Code Execution: (7:40)
Conclusion: (21:26)
______________________________________________________________________
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
______________________________________________________________________
![Classical Machine Learning [DoorDash Data Science Project]](https://i.ytimg.com/vi/pqQG_tNXncc/mqdefault.jpg "Classical Machine Learning [DoorDash Data Science Project]
In this video, well be diving deep into machine learning for data science. Well explore some of the most important concepts in machine learning and choose the best-performed model. Well demonstrate how to apply these concepts in practice on the DoorDash data science project Delivery Duration Prediction. Well apply 6 different algorithms, 4 different feature set sizes, and 3 different scalers.
Watch our previous video:
📌 Part 1: Data Preparation for Modeling: https://youtu.be/Sf6jn8QZHhc
📌 Part 2: Collinearity and Removing Redundancies: https://youtu.be/m3zEV10qvE8
📌 Part 3: Multicollinearity and Feature Selection: https://youtu.be/gh5JzALBQvU
🧑💻 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+classical+ML+doordash+project
👉 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+classical+ML+doordash+project
Timeline:
Intro: (0:00)
Take-home assignment from DoorDash: (0:23 )
In previous videos: (0:40)
Applying the data to the machine learning model: (1:14)
Applying regression tasks: (2:03)
Changing the problem: (5:17)
Extracting the Prep_duration predictions (7:20)
Deep Learning Method: (10:31)
Conclusion: (12:44)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+classical+ML+doordash+project), 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+classical+ML+doordash+project. 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 #MachineLearning")
