Your first Data-to-AI hire: Run a lovable process
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.
Analytics
Learn about Dataland to avoid expensive hiring mistakes
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.
Is your tech stack ready for data-intensive applications?
Questions to assess the quality of tech stacks and lifecycles, with a focus on artificial intelligence, machine learning, and analytics.
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.
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.
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.
Bootstrapping the right way?
Video and summary of a talk I gave at YOW! Data on bootstrap estimation of confidence intervals.
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.
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.
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.
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.
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.
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.