Compare perfect, monopolistic and oligopoly markets; find breakeven and shutdown points; see how pricing power changes with concentration and scale.

Unpack the 3 Vs (+ veracity) of Big Data, AI & ML toolkits, data pipelines,visualisation, and NLP—all tailored to quantitative investing.

Estimate slopes and intercepts with OLS, check key assumptions, interpretANOVA, R², and transform data (log‑lin, lin‑log, log‑log) for better fits.

Learn to verify co‑movement: t‑tests for Pearson correlation, Spearman rankfor monotonic links, and chi‑square for independence in contingency tables.

Master the six‑step test framework, Type I/II errors, power, and the bread‑and‑buttert‑, χ²‑, F‑ and non‑parametric tests used throughout finance.

From sampling designs to the central‑limit theorem, nail standard errors andbootstrap confidence intervals so you can quantify parameter uncertainty likea pro.

Discover lognormal price behaviour, Monte Carlo pricing and risk, andbootstrap resampling when history is all you have—key tools for modernquant finance.

Link single‑asset stats to the whole basket: compute portfolio expectedreturn, variance, covariance matrix, correlation, and shortfall risk—withstep‑by‑step examples.

Turn uncertainty into pictures: expected value, variance, probability trees,total‑probability rule, and Bayes’ formula for updating forecasts when newdata arrive.

Build your stats toolkit: mean, median, mode, quantiles, variance, standarddeviation, skew, kurtosis—plus an intro to covariance and correlation—so youcan summarise any return distribution with confidence.