Probability and Statistics for Financial Markets

Use data without mistaking probability for certainty

This course teaches the statistical foundations needed to evaluate financial and crypto-market information professionally. Students learn how to describe data, measure variation, identify outliers, interpret distributions and correlations, and test whether an apparent trading edge is supported by enough evidence.

Students will learn to:

Distinguish qualitative, quantitative, leading, coincident and lagging information.
Clean and organize financial datasets without mixing incompatible sources.
Interpret mean, median, mode, histograms and distribution shape.
Use variance, standard deviation and z-scores responsibly.
Recognize fat tails, outliers and the limits of normal-distribution assumptions.
Understand time series, stationarity, correlation, scatterplots and regression.
Use Monte Carlo simulation as a scenario tool rather than a forecast.

Educational notice: Statistical models simplify reality. Results depend on data quality, sample size and assumptions, and they do not guarantee investment outcomes.