Startup data health starts with healthy event tracking

Expanding on the startup health check question of tracking Kukuyeva’s five business aspects as wide events.

June 10, 2024

Assessing a startup's data-to-AI health

Reviewing the areas that should be assessed to determine a startup’s opportunities and challenges on the data/AI/ML front.

April 22, 2024

Substance over titles: Your first data hire may be a data scientist

Advice for hiring a startup’s first data person: match skills to business needs, consider contractors, and get help from data people.

February 5, 2024

Bootstrapping the right way?

Video and summary of a talk I gave at YOW! Data on bootstrap estimation of confidence intervals.

October 6, 2019

Defining data science in 2018

Updating my definition of data science to match changes in the field. It is now broader than before, but its ultimate goal is still to support decisions.

July 22, 2018

Customer lifetime value and the proliferation of misinformation on the internet

There’s a lot of misleading content on the estimation of customer lifetime value. Here’s what I learned about doing it well.

January 8, 2017

If you don’t pay attention, data can drive you off a cliff

Seven common mistakes to avoid when working with data, such as ignoring uncertainty and confusing observed and unobserved quantities.

August 21, 2016

Making Bayesian A/B testing more accessible

A web tool I built to interpret A/B test results in a Bayesian way, including prior specification, visualisations, and decision rules.

June 19, 2016

Why you should stop worrying about deep learning and deepen your understanding of causality instead

Causality is often overlooked but is of much higher relevance to most data scientists than deep learning.

February 14, 2016

This holiday season, give me real insights

Some companies present raw data or information as “insights”. This post surveys some examples, and discusses how they can be turned into real insights.

December 8, 2015