Math & Programming Foundations

The mathematics and tooling every quant builds on.

Quantitative finance is applied mathematics executed in code. This track lays the base: Python and its packaging toolchain, the linear algebra and probability that models are written in, statistical inference for working with data, NumPy and DataFrame fluency, SQL for real market data, and Git with Jupyter for reproducible, reviewable research.

First Steps

Python for Quant Work

Python's numerical ecosystem and modern packaging make it the default language of quant research and trading infrastructure.

Mathematics

Linear Algebra

Vectors, matrices, and eigendecomposition power covariance analysis, PCA, and the multivariate models of later tracks.

Probability

Random variables, distributions, expectation, and Bayes’ rule supply the language for returns, risk, and pricing.

Statistical Inference

Estimation, hypothesis testing, regression, and stationarity turn noisy market data into defensible claims.

Data in Practice

NumPy & Array Thinking

The ndarray's vectorization, broadcasting, and memory model are the performance habits all numerical Python builds on.

pandas & Polars

DataFrames index, align, and group messy market data; Polars adds speed and lazy evaluation at scale.

SQL & Data Wrangling

Joins, aggregations, and window functions pull clean features out of the warehouses where market data actually lives.

Workflow

Git & Jupyter Workflow

Git plus notebooks give research a versioned, reviewable, and shareable form without losing exploratory speed.