gdberrio's repositories
ad_response_tutorial
This tutorial shows users how to evaluate advertising response using last click attribution, experiments, marketing mix models and attribution models. By applying these methods to the same (synthetic) data set, users will learn how the methods compare. We also illustrate the data manipulation that is required to prepare typical raw advertising data for analysis. Examples are worked in R and slides are provided in LaTeX.
BDA_course_Aalto
Bayesian Data Analysis course at Aalto
blog
Personal Blog
coding-interview-university
A complete computer science study plan to become a software engineer.
data-science-machine-learning-ai-resources
A curated set of resources for data science, machine learning, artificial intelligence (AI), data and text analytics, data visualization, big data, and more.
docker-daemon
A Docker daemon to run in Fly and access via a WireGuard peer.
mmm_stan
Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
pymc-marketing
Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
pyprobml
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
PythonDataScienceHandbook
Jupyter Notebooks for the Python Data Science Handbook
ripgrep-all
rga: ripgrep, but also search in PDFs, E-Books, Office documents, zip, tar.gz, etc.