Practical insights on data science, statistics, and engineering—from statistical methods to data pipelines.
Hands-on examples and experiments with real-world data scenarios. We explore statistical methods, data engineering patterns, and machine learning techniques through concrete implementations.
From mixture models and hierarchical modeling to data pipeline design and infrastructure patterns. Each topic includes working code, visualizations, and practical takeaways.
When your data is a blend of multiple distributions and simple statistics fail you. Explore decomposition techniques and practical applications.
Hierarchical data structures and random effects in practice. Understanding when and how to apply mixed-effects modeling.
Understanding baselines and what "beating random" really means. Proper evaluation metrics for classification tasks.
Building robust data pipelines with patterns for table naming, data validation, and workflow orchestration in production environments.
Begin with mixture models to see practical approaches to decomposing complex distributions.
Explore Mixture Models