All you need is an idea and some data

Every data science project depends on two things: a clearly defined question and data to answer it.

Yet most data science blogs, articles, or even training courses focus almost exclusively on everything in between: coding, statistical methods, machine learning models or data visualisation.

Don't get me wrong, those aspects matter. But without good foundations (the data) and a well-defined target (the question), they become little more than soulless technical exercises.

The true added value of a data scientist depends on knowing what can be answered from what is available. Get those two right, and everything else (choosing the models, tools, language) starts to fall naturally into place.

The central idea you’ll find in these pages is that good data analysis depends more on mindset, creativity, and adaptability than on technique.

My notebooks document that process: the initial idea, its refinement into questions, the search for data, the inevitable mistakes, and the lessons learned along the way.

By reading these pages, I want to expose how I think and how I approach data problems. I won’t pretend my solutions are perfect. They’re simply the ones that made the most sense given the information I had at the time.

Welcome inside my brain.