Projects in Awesome Lists by boringPpl

https://github.com/boringppl/data-engineer-roadmap

Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups

Last synced: 09 Apr 2025

https://github.com/boringPpl/data-engineer-roadmap

Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups

Last synced: 15 Apr 2025

https://github.com/boringPpl/data-science-roadmap

Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups

Last synced: 01 Apr 2025

https://github.com/boringppl/data-science-roadmap

Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups

Last synced: 09 Apr 2025

https://github.com/boringppl/linkedin-profiles-scraping

Automatically scrape the web data of people profiles on Linkedin based on a specific search query

beautifulsoup beautifulsoup4 python python3 selenium selenium-webdriver webscraper webscraping webscraping-data webscrapper webscrapping

Last synced: 06 Mar 2026

https://github.com/boringppl/sales-reporting

Conduct a Report and Analysis on 200,000 sales data points to answer revenue-related questions for the business

Last synced: 25 Aug 2025

https://github.com/boringppl/presidential-debates-comments-clustering

At the point when we started this project, election week is coming up. There was so much excitement in the air on who is the next US president to be elected. There were thousands of articles on who's leading the polls. The US election has been trending on most, if not all, social media platforms. Being Data scientists, we wonder if it would be possible to leverage on these different data sources to understand various topics of discussion surrounding each candidate. Of which, we have decided to focus on Youtube comments as a starting point for this project.

biden comments-clustering donald-trump youtube-comments youtube-video

Last synced: 14 Aug 2025

https://github.com/boringppl/data-project-guideline-from-netflix

"Data science is such a nebulous term. To some, it means data analytics; to some it is synonymous to machine learning; others think there is a data engineering flavor to it. The wide spectrum of possible responsibilities and the nuanced differences across companies or even teams within the same company make the identity evasive. You literally have to speak to a data scientist in company X to understand how company X sees data science. This guidline is inspired by a Netflix talk with a focus on the structure of a data science projects."

Last synced: 12 Apr 2025