Learn the probability and statistics used in quantitative research, from distributions and estimation to hypothesis testing, regression and resampling, applied to market data.
Duration6 weeks
LevelIntermediate
Modules6
Lessons24
This course covers the statistical reasoning that quantitative researchers rely on every day. You will study probability distributions, estimation, hypothesis testing and regression, with special attention to fat tails, dependence and the multiple testing problems that arise when many ideas are tested on the same data. Each topic is grounded in Python exercises using real return series so you learn to separate signal from noise with appropriate skepticism.
6 modules · 24 lessons · 6 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Profile the return distributions of several asset classes and compare tail behavior with a normal benchmark
Test whether a set of candidate signals has explanatory power after adjusting for multiple comparisons
Use block bootstrap resampling to build confidence intervals for a backtest's Sharpe ratio
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.
6 weeks6 modulesBuild the core mathematics behind quantitative finance, including calculus, linear algebra, optimization and an introduction to stochastic processes, with Python exercises.
8 weeks8 modulesModel financial time series with stationarity tests, ARIMA, GARCH, cointegration and state-space methods, and evaluate forecasts with disciplined out-of-sample testing.
7 weeks7 modulesApply rigorous validation methods to trading strategies: walk-forward and cross-validation, overfitting diagnostics, multiple testing control and robust parameter optimization.
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