Skip to content EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas
! Welcome back to the “EDA in Public” series! This is Part 2 of the series; if you haven’t seen ...
Read more Understanding the Generative AI User | Towards Data Science
in some interesting conversations recently about designing LLM-based tools for end users, and one of the important product design questions ...
Read more The Machine Learning “Advent Calendar” Day 19: Bagging in Excel
For 18 days, we have explored most of the core machine learning models, organized into three major families: distance- and ...
Read more 4 Ways to Supercharge Your Data Science Workflow with Google AI Studio
Gemini 3 models into Google AI Studio, I’ve been experimenting with it quite a bit. In fact, I find the ...
Read more The Machine Learning “Advent Calendar” Day 18: Neural Network Classifier in Excel
Neural Network Regressor, we now move to the classifier version. From a mathematical point of view, the two models are ...
Read more A Practical Toolkit for Time Series Anomaly Detection, Using Python
fascinating aspects of time series is the intrinsic complexity of such an apparently simple kind of data. At the end ...
Read more Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach
grow more complex, traditional logging and monitoring fall short. What teams actually need is observability: the ability to trace agent ...
Read more When (Not) to Use Vector DB
. They solve a real problem, and in many cases, they are the right choice for RAG systems. But here’s ...
Read more The Machine Learning “Advent Calendar” Day 15: SVM in Excel
we are. This is the model that motivated me, from the very beginning, to use Excel to better understand Machine ...
Read more 6 Technical Skills That Make You a Senior Data Scientist
be honest. Writing code in 2025 is much easier than it was ten, or even five, years ago. We moved ...
Read more 
