Analytics

Video and key points from the second part of a webinar on a startup’s first data hire, covering tips for defining the role and running the process.

Video and key points from the first part of a webinar on a startup’s first data hire, covering data & AI definitions and high-level recommendations.

Questions to assess the quality of tech stacks and lifecycles, with a focus on artificial intelligence, machine learning, and analytics.

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

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

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

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

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.

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

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

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

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

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