Learn the foundations of financial data engineering: data types, schemas, SQL, ingestion, cleaning, corporate actions and reliable batch pipelines in Python.
Duration5 weeks
LevelIntermediate
Modules5
Lessons20
This course covers how financial data is structured, stored and moved so that research and applications can trust it. You will model prices, reference data and corporate actions in SQL, write Python ingestion and cleaning jobs, and handle the common pitfalls of financial datasets such as time zones, adjustments and survivorship. By the end you will have a small, well-tested batch pipeline feeding a research-ready database.
5 modules · 20 lessons · 5 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Design and populate a PostgreSQL securities master with prices and corporate actions
Build an idempotent ingestion job that loads daily price files and logs quality issues
Create a data quality report that flags gaps, outliers and stale prices
Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.
7 weeks7 modulesBuild batch and streaming market data pipelines in Python with Kafka, PostgreSQL and Parquet, covering normalization, bar aggregation, quality checks and replay.
6 weeks6 modulesBuild dependable data collection and storage for financial applications: consuming REST and WebSocket APIs, validating data, and orchestrating pipelines into PostgreSQL.
5 weeks5 modulesTurn raw market and fundamental data into reliable insights with pandas, statistics and visualization, emphasizing data quality, reproducibility and honest interpretation.
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