The Python versus R debate has been mostly settled by the market, though you’ll still find passionate R advocates in academic statistics departments. For anyone building a data science career in industry — in banking, telco, fintech, or any commercial organisation — the practical answer is clear: learn Python.
But the reason matters, and it’s worth understanding precisely why Python has won.
What R was built for — and where it still excels
R was designed by statisticians for statistical analysis. Its foundations are genuinely excellent: expressive statistical computing primitives, a vast distribution of functions, and ggplot2 — still one of the best data visualisation tools available in any language.
For pure statistical analysis — econometrics, clinical trials, actuarial work, academic research — R remains formidable. If you’re going into quantitative research or academic statistics, R is worth learning seriously alongside Python.
Why Python has won in industry
Python is a general-purpose language. That’s the entire answer.
When you need to scrape a website, call an API, build a web application, write a pipeline, deploy a model, containerise a service, or integrate with a data warehouse — Python has libraries for all of it, and they’re production-grade.
R’s production story is weak. Deploying R models into commercial production environments is possible, but it requires additional infrastructure that Python avoids entirely. In regulated industries like banking, where production deployments go through formal validation, this matters significantly.
The ML ecosystem also converged on Python. PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain — the entire modern ML stack is Python-first. Trying to do serious deep learning or LLM work in R is not practical.
The practical answer
Learn Python. Start with pandas, NumPy, and scikit-learn. Build something end-to-end — from raw data to a deployed artefact — before touching deep learning.
If you’re in academia or moving toward statistical research, learn R as a second language. The base is excellent and the cost of learning it once you have Python is low.
If you’re in industry, Python is the language. Learn it properly.