Data science
Evidence, causality and the honest use of data.
7 essays, 2013–2020.
Essays7
- What Can COVID-19 Forecasters Learn from Pascal’s Wager
In this article, we analyze a forecast that locked down a nation and show how easy to make small mistakes in modeling lead to big problems in outcomes.
- Data as Cost — Big Cakes are Nice if you Like Cake
If data is cost, and algos are a commodity, what is it that we should value in the coming age of automated decision making?
- Five Essential Points on Data Visualization
The goal of data visualization is to act as a catalyst for some sort of behavior change, yet mostly practitioners focus on other things.
- Entropy and Invalid Traffic Detection
For the past 16 months we have worked on analyzing daily ad exchange bid logs with the goal of creating a “signals intelligence” scoring mechanism that could handle 200 billion bid events…
- Demystifying Causality — How to Move from Guesswork to Knowing
If researchers agree that cause and effect is important, why is it not the standard and correlation is — the blocker, is it philosophical or logistical?
- An autopsy of a data scientist
A working life divided between learning, wrangling, management, and the possibility of genuinely creative work.
- Just Enough Manifesto: Don’t Drink the Big Data Kool-Aid
Data has gotten heads spinning. It’s too many in kind and too much in quantity. Data is becoming a big problem. More data means more cost, and more inputs lead to more complexity…