Turn raw market and fundamental data into reliable insights with pandas, statistics and visualization, emphasizing data quality, reproducibility and honest interpretation.
Duration5 weeks
LevelIntermediate
Modules5
Lessons20
This course focuses on the analytical layer between raw data and research decisions. You will clean and align price, volume and fundamental datasets, explore distributions and relationships, run regressions with statsmodels and build cross-sectional screens. The course stresses common analytical traps in financial data, such as survivorship bias, look-ahead bias and spurious correlation, so that conclusions are defensible.
5 modules · 20 lessons · 5 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Build a data quality report that flags gaps, outliers and stale prices across a multi-instrument dataset
Run a regression study of a stock's returns against market and sector returns with full diagnostics
Create a point-in-time fundamental screen and document the steps taken to avoid look-ahead bias
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.
5 weeks5 modulesLearn Python from the ground up through financial examples: core syntax, pandas time series, returns calculations, plotting and clean, testable code.
7 weeks7 modulesApply supervised and unsupervised machine learning to financial datasets using feature engineering, purged cross-validation and disciplined, leakage-aware evaluation.
5 weeks5 modulesLearn the foundations of financial data engineering: data types, schemas, SQL, ingestion, cleaning, corporate actions and reliable batch pipelines in Python.
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