For an article on A/B testing that I am preparing, I asked my partner Dr. Nina Zumel if she could do me a favor and write some code to produce the diagrams. She prepared an excellent parameterized diagram generator. However being the author of the book Practical Data Science ...

An infinity of ML-based time series forecasting models with uncertainty quantification

Jupyter notebooks are a useful tool for Python users of all levels. They allow us to mix together plain text (formatted as Markdown) with Python code. This is beneficial for beginners and experien...

After a pandemic-induced hiatus and move to fully virtual, the Global Excel Summit is BACK in hybrid format this February 2024, live from London, UK. I’m super excited to participate in person at this year’s event. Would you like to join me? Use promocode GEORGE at checkout to get 10% ...

Univariate and multivariate time series forecasting with uncertainty quantification in Python

In this post I’m going to show you my process for solving regression problems with XGBoost in python, using either the native xgboost API or the scikit-learn interface. This is a powerful methodology that can produce world class results in a short...

As mentioned in the first post in this series, this post will cover how to build a Mastodon bot. This can be fairly straightforward, but - as always - there are multiple ways to “make it work”. Fortunately, there are already some fantastic tutorials out there that can help you ...

Our bags are packed, flights are booked, and we’re ready to head stateside for posit::conf(2023). We’re excited to be sponsoring the event this year, as well as presenting a few talks ...

In today's rapidly evolving technological landscape, the need for handling multiple databases is more pressing than ever. Whether to enhance redundancy, optimize performance, test correctness of the new solution or cater to varied data storage requirements, employing multiple databases can offer significant advantages.