Tutorials
In-depth guides to product analytics, statistics, experimentation, and machine learning, adapted from courses I've taught
Introduction to Product Analytics
Part 1 of the product analytics series: what product analytics is, how metrics and ratios work, what makes a metric g...
Analyzing Declining Metrics
Part 2 of the product analytics series: an eight-step process for diagnosing a metric drop, from confirming it's real...
The Company Words Keep: How Word2Vec Learns Word Embeddings
A beginner-friendly walkthrough of Skip-gram word embeddings, using a toy geometric analogy before showing how Word2V...
Beyond Semantics: A Value-Driven Approach to Word Embeddings
This article introduces a novel value-driven word embedding model that transcends traditional semantic approaches. By...
Neural Networks Demystified: Transformations Through Embedding Spaces
An intuitive guide to how neural networks work. Each neuron is a pattern detector: its weights describe a pattern, it...
Beyond Keywords: Personalizing Contextual Advertising with Audience-Aware Embeddings
This article outlines how we improved contextual advertising by embedding webpage content and fine-tuning it using au...
Measuring Success: A Structured Framework
Part 3 of the product analytics series: a seven-step framework for answering "how's the product doing?", from scope a...
Feature Impact Analysis
Part 4 of the product analytics series: a seven-step process for finding out whether a feature actually worked, using...
Mastering Trade-Off Analysis
Part 5 of the product analytics series: an eight-step framework for balancing competing metrics like speed and conver...
A/B Testing & Statistical Thinking with 2022 BRFSS Data
A practical walkthrough of the statistics behind A/B testing, framed around public health interventions and the CDC's...
Statistics Foundations: Mean, Spread, Sampling, and Your First Hypothesis Tests
A beginner-friendly tour of the statistics behind data science and A/B testing: central tendency, spread, sampling an...
Introduction to Machine Learning: Understanding Supervised Learning
A thorough first look at supervised learning that treats data as geometry: machine learning vs. traditional programmi...
Classification Models: Logistic Regression, k-NN, Decision Trees, and Naive Bayes
A hands-on comparison of four classic classifiers on the Breast Cancer Wisconsin dataset using scikit-learn. It cover...
Predicting 30-Day Hospital Readmission for Diabetic Patients
The design of a hands-on biomedical data science project, built for the RCMI Summer Data Science Research Training pr...
Recurrent Neural Networks: Memory as a Moving Point in Space
An intuition-first guide to recurrent neural networks: the hidden state as a point that moves with every word, what e...