Uploaded December 2020 | Updated September 2026, 2 days ago
We're going to apply software engineering fundamentals for this common data science tasks where we are going to finish automating file imports to a database. In this last installment of this video series, I'll show you how to transform all the code into functions and separate them out from your main python file. This basically creates your own custom library of functions you can use for any future project.
I recorded 2 other parts of this series where I show you how to create the functionality and automate the python script.
- Part 1: Functionality (youtu.be/wqBFgaMgFQA)
- Part 2: Automation ( youtu.be/TDwy1lSjEZo)
Here's also the github repo of the finished project:
- github.com/Strata-Scratch/csv_to_db_automation
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Resources:
How to setup Anaconda: docs.anaconda.com/anaconda/install
How to install psygopg2: anaconda.org/anaconda/psycopg2
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Practice real data science interview questions: platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
Timestamps:
Intro: (0:00)
Breakdown of the script: (1:15)
Creating the CSV functions: (3:05)
Creating the cleaning functions: (10:50)
Creating the database import functions: (17:22)
Separating the functions from the main.py: (23:40)
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
______________________________________________________________________
We're going to apply software engineering fundamentals for this common data science tasks where we are going to finish automating file imports to a database. In this last installment of this video series, I'll show you how to transform all the code into functions and separate them out from your main python file. This basically creates your own custom library of functions you can use for any future project.
I recorded 2 other parts of this series where I show you how to create the functionality and automate the python script.
- Part 1: Functionality (youtu.be/wqBFgaMgFQA)
- Part 2: Automation ( youtu.be/TDwy1lSjEZo)
Here's also the github repo of the finished project:
- github.com/Strata-Scratch/csv_to_db_automation
______________________________________________________________________
Resources:
How to setup Anaconda: docs.anaconda.com/anaconda/install
How to install psygopg2: anaconda.org/anaconda/psycopg2
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Practice real data science interview questions: platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
______________________________________________________________________
Timestamps:
Intro: (0:00)
Breakdown of the script: (1:15)
Creating the CSV functions: (3:05)
Creating the cleaning functions: (10:50)
Creating the database import functions: (17:22)
Separating the functions from the main.py: (23:40)
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
______________________________________________________________________
![Advanced Facebook Data Science SQL interview question [RANK()]](https://i.ytimg.com/vi/PlpUo6bHsBQ/mqdefault.jpg "Advanced Facebook Data Science SQL interview question [RANK()]
This advanced SQL question is from the Facebook data science interview that tests your ability to write window functions and rank data and use some advanced functions like coalesce. It’s a complicated question that involves subqueries and joins and also tests your knowledge in rankings.
Link to the question to follow along with me: https://platform.stratascratch.com/coding/2007-rank-variance-per-country?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: (0:33)
Framework to solve the problem: (0:58)
Understand your data: (2:18)
Formulate your approach: (5:38)
Code Execution: (8:42)
Conclusion: (23:05)
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
#FacebookDataScienceInterview")
![Advanced Data Science SQL Interview Question [Amazon] (window functions & aliasing)](https://i.ytimg.com/vi/QenwDm5oWdU/mqdefault.jpg "Advanced Data Science SQL Interview Question [Amazon] (window functions & aliasing)
This SQL data science interview question was asked by Amazon and will test your date manipulation and window function skills. I’ll cover both the question and walk you through the approach. I’ll also talk about my 4 step approach to solving any data science interview question. This is literally how I would answer every data science interview question and prepare for every data science interview at FAANG companies and others.
Link to the question: https://platform.stratascratch.com/coding/10319-monthly-percentage-difference?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
This series is for both beginner and intermediate data scientists and analysts interested in learning how to solve common data science interview questions in SQL. These are real data science interview questions. For some background context and an intro about what this series is about: https://bit.ly/36kKbxG
👉 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)
Question: (1:18)
4-Step framework to solve the question: (2:02)
Explore underlying data: (2:18)
Identify required columns: (3:45)
Visualize the output: (4:12)
Build solution step-by-step and test: (4:44)
Coding: (4:57)
Format data to YYYY-MM: (6:27)
Calculate current months revenue: (7:46)
Calculate previous months revenue: (8:36)
Aggregate to year-month: (10:25)
Implement month-over-month difference formula: (11:26)
Apply a window alias: (13:15)
Clean up formatting: (14:06)
Conclusion: (14:50)
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.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#datascienceinterview #sqlinterviews")
![Data Project: Analyzing Review Sentiment for Accurate Ratings [Part 1]](https://i.ytimg.com/vi/Qh9ajFNeIEc/mqdefault.jpg "Data Project: Analyzing Review Sentiment for Accurate Ratings [Part 1]
Welcome to the second series of the Facebook data science project for predicting movie ratings! In this video, we explore building a high-performance classification algorithm to predict movie ratings. We merge two data sets, focusing on review sentiment to determine whether a movie is labeled as Rotten or Fresh. Join us as we preprocess the data, analyze the distribution of review types, and implement a random forest model. Dont forget to subscribe and stay tuned for the next video in our series!
Go to the project through the link below and follow along with us👇
https://platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p1
Watch the previous videos from the first series:
📌 [Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings: https://youtu.be/cM12QtrhdLo
📌[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings: https://youtu.be/Ih6G5hnn30Q
📌 [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings: https://youtu.be/J4rheMtvu6g
👉 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
👉 Playlist for data science projects: https://bit.ly/StrataScratchProjectsYouTube
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p1
Timeline:
Intro: (0:00)
Take-home assignment: (0:18)
Approaches review: (0:31)
Preparing the dataset: (1:10)
Using the head method for data review: (2:34)
Encoding the labels into integer representation: (3:45)
Splitting the dataset into train and test sets: (4:30)
Tokenization in NLP: (4:53)
Using a random forest classifier: (6:21)
Improving the models performance: (7:33)
Take away: (9:24)
About The Platform:
StrataScratch (https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p1) is 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?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+s2+p1. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from the StrataScratch team, 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 us at team@stratascratch.com
#datascienceproject")
