A short reflection on why we teach both languages — and how the skill stack of a working quant has changed over the last decade.
Ten years ago, you could be a useful quant with strong Excel, working VBA, and one of MATLAB or R. Five years ago, Python had taken over the data-science side, but the trading floor still spoke C++ for low-latency and R for statistics. Today, the working quant ships in Python, prototypes in R, reads scikit-learn / PyTorch / statsmodels source as comfortably as a textbook, and integrates LLMs into the workflow without breaking stride.
The case for teaching both
MCF teaches both Python and R because the day-to-day job of a quant uses both — and because the conceptual model of each language sharpens the other. Python rewards composition and tooling discipline. R rewards mathematical clarity and statistical literacy. The pairing is a feature, not a redundancy.
The full stack
The rest of the stack flows from there: SQL for the data, git for the work, Docker for the deployment, a familiarity with one cloud (AWS for most banks, GCP for fintech). Add ML frameworks and a working sense of when to reach for an LLM versus a classical model, and you have the skill stack of an effective quant in 2026.