Data Adventures
Data analytics and data science made practical
17 articles and counting
How Bad Data Hides: A Guide to Data Quality
Bad data doesn’t always announce itself. Silent failures like label noise and distribution drift can degrade models long after deployment. This guide covers systematic auditing, principled cleaning, and production validation to catch data quality issues before they cost you.
When Does More Data Hurt? How to Prioritize Data Quality Over Volume
Collecting more data is often the instinct, but more data with poor labels can make models worse. This post explains how to audit data, detect label noise, and decide when to prioritize quality before scaling.