Blog
Posts on A/B testing, analytics practice, entrepreneurship, hiring, and selected industry topics.
How subscription metrics - ARR, ARPPU, retention, LTV, CAC, and unit economics - connect into one system, with formulas, worked examples, and the decisions each metric informs.
A practitioner's guide to the 10 machine learning algorithms that handle the majority of real-world tabular, text, and classification problems, with decision criteria and production tradeoffs.
Data-backed analysis of startup success and failure: modern risk frameworks, market validation, the first-mover myth, Lean Startup evolution, and how AI changed the math.
How confidence intervals, Z-scores, and statistical significance actually work in A/B testing, with worked examples and Python code.
A plain-language explanation of A/B testing fundamentals — control groups, test groups, confidence intervals, statistical significance, and common pitfalls.
Five practical tips on projects, resumes, and recruiter conversations for data science roles.