Uploaded February 2023 | Updated September 2026, 3 days ago
This video will walk you through Whole Foods Market's 'Brand Segmentation based on Customer Activity' Python interview question solution. We'll use the groupby() and agg() functions to solve a difficult coding question and demonstrate how to use these functions to solve real-world problems.
🧑💻 Go to the question through the link below and follow along with me:
platform.stratascratch.com/coding/2038-wfm-brand-segmentation-based-on-customer-activity?utm_source=youtube&utm_medium=click&utm_campaign=YT+group+by+aggregate+functions
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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+group+by+aggregate+functions
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Timeline:
Intro: (0:00)
Interview Question: (0:30)
Exploring and understanding the dataset: (0:39)
Writing out the approach: (2:14)
Coding the solution: (4:07)
Conclusion: (9:48)
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About The Platform:
I'm using StrataScratch (platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+group+by+aggregate+functions), 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+group+by+aggregate+functions. 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 #PythonInterviewQuestion #PythonCodingInterview
This video will walk you through Whole Foods Market's 'Brand Segmentation based on Customer Activity' Python interview question solution. We'll use the groupby() and agg() functions to solve a difficult coding question and demonstrate how to use these functions to solve real-world problems.
🧑💻 Go to the question through the link below and follow along with me:
platform.stratascratch.com/coding/2038-wfm-brand-segmentation-based-on-customer-activity?utm_source=youtube&utm_medium=click&utm_campaign=YT+group+by+aggregate+functions
______________________________________________________________________
👉 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+group+by+aggregate+functions
______________________________________________________________________
Timeline:
Intro: (0:00)
Interview Question: (0:30)
Exploring and understanding the dataset: (0:39)
Writing out the approach: (2:14)
Coding the solution: (4:07)
Conclusion: (9:48)
______________________________________________________________________
About The Platform:
I'm using StrataScratch (platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+group+by+aggregate+functions), 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+group+by+aggregate+functions. 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 #PythonInterviewQuestion #PythonCodingInterview
![Working with APIs in Python [For Your Data Science Project]](https://i.ytimg.com/vi/fklHBWow8vE/mqdefault.jpg "Working with APIs in Python [For Your Data Science Project]
We’re going to be working with the Youtube API to collect video statistics from my channel using the requests python library to make an API call and save it as a pandas dataframe. Working with APIs is a necessary skillset for all data scientists and should be incorporated into your data science projects. I talk about the one data science project you’ll ever need in this video https://bit.ly/3rEt6WG so we’ll start with the first step and learn how to work with APIs in python to collect our data.
The python notebook and links to resources are located in this Github repo: https://github.com/Strata-Scratch/api-youtube/blob/main/README.md
Link to the video referred to in the Intro: https://www.youtube.com/watch?v=c4Af2FcgamA
👉 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)
Coding on Google Colab: (2:00)
Testing with the Requests Library: (4:16)
Working with the YouTube API: (6:32)
Response from Making API Call: (11:00)
Data is in the items Key: (12:22)
Parsing through the Data: (12:57)
Creating the Loop: (16:17)
Making a Second API Call: (18:30)
Saving to a Pandas DataFrame: (20:31)
Implementing Good Software Engineering Fundamentals: (22:40)
Conclusion: (27:03)
If you want data science interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT%20description%20link&utm_content=APIs%20in%20Python. 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
#PythonAPI")
![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")
