Building a Data-Driven Trading Operation
From raw data to measured risk.
Lay the data foundation, then layer quantitative method and volatility analytics on top — and close with the control framework a real trading desk needs.
An introduction to data engineering: the lifecycle, modern tools, architectural patterns, and key concepts glossary. Covers ingestion, storage, transformation, orchestration, and data quality.
SQL is the universal language of data. This beginner module covers essential SQL concepts every data engineer needs: SELECT queries, JOINs, aggregations, CTEs, window functions, and query optimization basics.
Learn dbt fundamentals (models, tests, docs, sources), Dagster's software-defined asset approach, and how to build end-to-end pipelines combining both tools with best practices and a comparison of orchestration frameworks.
Foundation of quantitative finance — statistical methods, risk measures, and multi-factor models used in institutional portfolio management.
Understand volatility as an asset class — from historical vol to implied vol surfaces, VIX dynamics, and volatility trading strategies.
Create a complete AML compliance program: risk assessment methodology with weighted scoring, transaction monitoring rule design, SAR governance frameworks, fintech compliance case study, training program design, and regulatory exam preparation. Advanced level.
Journey complete — well done! Review your progress in the Study Queue.