Thoughts on data science, statistics and machine learning.
Book Review: Invisible Women by Caroline Criado Perez
I learnt long ago that throwing data at people doesn’t change their opinions. After reading this book, I’m inclined to think that I might have been wrong. I wish I could carry around several hardbound editions of this book and throw them at anyone who says or does anything sexist. A well-aimed hardback to the bridge of the nose could work magic. And it would count as throwing data, too. Of the 400 pages of this book, 70 are just the endnotes.
Bayesian Storytelling
I launched a newsletter yesterday. So far, the feedback has been good. A few readers said that they felt drawn in by the writing. In any case, the purpose of the first few posts is simply to get myself warmed up. Any extra flutter the posts generate is a bonus. Amit Varma recommends not looking at the stats for a couple of years. It taught me quite a few things. Particularly that I need to be smart about the data analysis.
More Pixels
Someone must have thought, likely with good reason, that more pixels on larger screens is a good idea. Then, someone else must have thought that if it’s a good idea somewhere, it must be a good idea everywhere. It’s probably for the same reason that I can’t find a new car with tactile switches anymore. Everything’s got touch buttons. In my car, nothing other than the steering wheel has any physical feedback.
Beer on the Mekong - Growth, Discovery and Creativity in 2025
It’s easy to dismiss New Year’s Day as just another revolution around the sun - an arbitrary checkpoint which carries no inherent meaning. But then all checkpoints and milestones are arbitrary. Who’s to say that 17 year and 11 months old teenager is significantly less mature than an 18 year-old adult? We have milestones because they’re convenient. So in the spirit of benchmarking convenience, perhaps it is not such a bad idea to stick to resolutions, goals and plans.