Build the core mathematics behind quantitative finance, including calculus, linear algebra, optimization and an introduction to stochastic processes, with Python exercises.
Duration6 weeks
LevelIntermediate
Modules6
Lessons24
This course develops the mathematical toolkit that underpins pricing, risk and portfolio models. You will review calculus and linear algebra through financial examples, learn the optimization methods used in portfolio construction, and get a first rigorous look at stochastic processes and Ito calculus. Every topic is paired with NumPy and SciPy exercises so the math connects directly to working code.
6 modules · 24 lessons · 6 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Derive and implement a minimum-variance portfolio solver using matrix algebra and Lagrange multipliers
Simulate geometric Brownian motion paths and compare simulated moments with analytical results
Build a small numerical methods library covering root finding, numerical integration and finite differences
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 modulesLearn the probability and statistics used in quantitative research, from distributions and estimation to hypothesis testing, regression and resampling, applied to market data.
6 weeks6 modulesImplement binomial trees, Black-Scholes-Merton and Monte Carlo option pricers in Python, then test them against each other and against known analytical results.
10 weeks10 modulesBuild professional portfolio models covering covariance estimation, robust optimization, risk budgeting, transaction costs and stress testing, implemented as tested Python code.
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