financial-data-science

You enforce schema contracts and freshness tracking by defining data source contracts, schema versions, and freshness objectives, then continuously monitoring quality metrics to quarantine degraded feeds before they impact downstream models.

Deterministic ingestion and validation is achieved by ingesting data with replay support and deterministic normalization, which explicitly validates keys, timestamps, and join behavior to ensure reproducible quantitative research.

Lineage completeness prevents errors by enforcing strict release criteria that only publish data when lineage, ownership, and quality thresholds are fully satisfied, stopping inaccurate data from reaching production controls.

Yes, you can use pandas as the core dependency to run financial data science diagnostics, automating the validation and normalization of input CSV files to produce reproducible research outputs.

You need continuous monitoring and quarantine when dealing with critical financial market data, as it isolates degraded feeds that fail quality thresholds, preventing costly errors in risk models or trading algorithms.

You run diagnostics by executing the financial data science diagnostics script on an input CSV file named 'market_data.csv', which evaluates lineage and quality metrics and saves the output to 'diagnostics.json'.