Building production-grade data science models requires a disciplined engineering foundation paired with deep domain knowledge.
The Data Pipeline Lifecycle
From ingestion, cleaning, and normalization to feature extraction and cross-validation, every step in the pipeline directly dictates final prediction accuracy.