A company does not trust data because it sits in a governed platform. It trusts data when people can find it, understand it, verify where it came from, determine whether it is fit for purpose, and know who is accountable when it fails. I first worked with this distinction properly at the National Research…

Tomorrow I start teaching a six-week data science bootcamp with iXperience, a data science programme endorsed by the Gordon Institute of Business Science. It is the same course I have taught before, but this time I am running it differently. This article is partly an explanation of why. When I first started teaching data…

I recently published a public GitHub repository based on the core analytical themes of my PhD research in climate variability, vegetation dynamics, and remote sensing.

A practical guide to understanding how Databricks Repos and Git actually interact — and how to safely commit, push, and merge code in a team environment.

Books that have changed my perspective on how I analyse, interpret and present data.

Reproducible experiment tracking is what separates working models from production-ready systems.

We do not lack climate data; we lack disciplined communication of what it means.

Excellence in data science comes from disciplined delivery, not just sophisticated models.