Abstract
Up and Down the Ladder of Abstraction
The most powerful way to gain insight into a system is by moving between levels of abstraction.
The way things move and spread, somewhat chaotically, across a network.
Complex systems are intrinsically hazardous systems.
Time
UTC is enough for everyone… right?
Programming time, dates, timezones, recurring events, leap seconds… everything is pretty terrible.
Storing UTC is not a Silver Bullet
That’s the bigger point, that goes beyond dates and times and time zones: choosing what information to store, and how. Any time you discard information, that should be a conscious choice.
A graph showing local time against universal time is a useful thing to draw if you have to work on some timezone-sensitive system and need help visualising all the things that might occur.
Reading / Writing
Leek Lab’s guide to reading academic papers
- I.e. how to keep up with a field/industry.
Anyone can publish an essay on the Web, and it gets judged, as any writing should, by what it says, not who wrote it. Who are you to write about x? You are whatever you wrote.
I try to write using ordinary words and simple sentences.
It is really incredible the amount of pushback I see from companies, startups to big, about writing. In particular around the notion that writing is the antithesis of agile. Writing ossifies and cements decision or plans that should change, it is said. My view is that agility comes from planning. Without plans, activities are just brownian motion. And you can’t have plans, especially shared plans, without writing.
Random Data History Things
The Future of Data Analysis (1962)
What of the future? The future of data analysis can involve great progress, the overcoming of real difficulties, and the provision of a great service to all fields of science and technology. Will it? That remains to us, to our willingness to take up the rocky road of real problems in preference to the smooth road of unreal assumptions, arbitrary criteria, and abstract results without real attachments. Who is for the challenge?
Data science vs. statistics: two cultures?
To first order, we summarize the critiques of statistics as: too much theory, not enough computation.