SciPy is the toolbox behind most numerical finance code. This skill teaches the parts a quant uses every week, with short checks (numbers you compute, steps you order, tools you match) and finance examples: why a normal curve understates the 1% loss, how Newton and bracketing methods find a bond yield, what a robust loss does to an outlier quote, why a cubic spline should not be extrapolated, how a forward curve becomes a discount factor, why Black–Scholes needs the normal function and vega, why you solve instead of inverting, when sparse storage pays, why a zero-phase filter cannot be traded and how clusters map diversification. The capstone is a code lab where you build the Nelson–Siegel curve, bond prices and yields, implied volatilities and a smile fit, and run them on real data.
Comfort with Python and NumPy is expected, together with school-level calculus and basic statistics.
