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.
How Do You Manage Assumptions in the Data Science Pipeline?
Every data scientist has trained a model that works perfectly in the notebook, then fails in production. The problem rarely lies in the algorithm itself—it’s hidden in upstream decisions about data collection, preprocessing, and validation. This guide reveals where those assumptions compound and how to catch them.