Boringppl.com
We connect you with study groups of people who share the same learning goals and side gigs as you
Pinned Repositories
Auto-Send-Linkedin-Connect-Request
Automatically send Linkedin invites with personalized messages to a database of targeted profiles.
boringppl-meeting-summarization
data-engineer-roadmap
Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups
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."
data-science-roadmap
Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups
deeplearning-roadmap
Deep Learning path with multiple notebooks
Linkedin-profiles-scraping
Automatically scrape the web data of people profiles on Linkedin based on a specific search query
Machine-Learning-with-Python
Python codes for common Machine Learning Algorithms
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.
Sales-Reporting
Conduct a Report and Analysis on 200,000 sales data points to answer revenue-related questions for the business
Boringppl.com's Repositories
boringPpl/data-engineer-roadmap
Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups
boringPpl/data-science-roadmap
Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups
boringPpl/Linkedin-profiles-scraping
Automatically scrape the web data of people profiles on Linkedin based on a specific search query
boringPpl/Sales-Reporting
Conduct a Report and Analysis on 200,000 sales data points to answer revenue-related questions for the business
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.
boringPpl/Machine-Learning-with-Python
Python codes for common Machine Learning Algorithms
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."
boringPpl/deeplearning-roadmap
Deep Learning path with multiple notebooks
boringPpl/Auto-Send-Linkedin-Connect-Request
Automatically send Linkedin invites with personalized messages to a database of targeted profiles.
boringPpl/boringppl-meeting-summarization
boringPpl/flownote-dockers
boringPpl/art_of_data_visualization
The art of effective visualization of multi-dimensional data
boringPpl/k8s-gateway
boringPpl/Crash-Course-on-Python
A comprehensive curriculum of Python programming foundation
boringPpl/hasbrain-helper-files
Helper files for hasBrain notebooks
boringPpl/python-fundamentals
Introductory Python Series for UC Berkeley's D-Lab
boringPpl/Rename-and-Organize-files-directories
Toy problem: Practice generating fake data; then, rename and organize the data into folders of their genres
boringPpl/textbook
Textbook for Data 100 at UC Berkeley
boringPpl/a-2017
Public Repository for cs109a, 2017 edition
boringPpl/Creating-Customer-Segments
Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.
boringPpl/CS229-notes
boringPpl/ecc
Edge-Conditioned Convolutions on Graphs
boringPpl/Fruit-Images-Dataset
Fruits-360: A dataset of images containing fruits
boringPpl/introtodeeplearning
Lab Materials for MIT 6.S191: Introduction to Deep Learning
boringPpl/notebook_code_execution
For users of Flownote who wants to understatnd the end to end flow for execution of code
boringPpl/Notes
boringPpl/Organize-folders
boringPpl/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
boringPpl/Stock_market_index_creation
boringPpl/WIBD-Workshops-2018