Is Python still the dominant language for Data Science or should I learn R in 2024?

All answers to this question.

Python is the clear winner for industry roles because of its "production-ready" nature. While R is beautiful for data visualization with ggplot2 and deep statistical analysis, Python’s ecosystem (Scikit-learn, TensorFlow, PyTorch) integrates seamlessly into web applications and cloud pipelines. Most tech companies prefer Python because a Data Scientist’s model can be easily handed off to a Software Engineer. If you are aiming for a role in pharmaceuticals or pure academia, R might have an edge, but for 90% of Data Science jobs, Python is the standard. I switched from R to Python two years ago and my job prospects tripled almost overnight.

   Answered 2025-03-16 by Barbara Miller

Do you think the rise of specialized libraries like Reticulate, which allows you to run Python inside R, makes the "one or the other" argument less relevant today? Why not just use the best tool for the specific task at hand?

   Answered 2025-03-18 by Charles Wilson

• Charles, while you can technically use both, most teams want a unified codebase for maintainability. Using Reticulate in a production environment can create dependency nightmares. I’ve found that sticking to Python for the entire pipeline, from ETL to deployment, keeps the workflow much cleaner and easier for the DevOps team to support.

     Commented 2025-03-19 by Kimberly Thompson

Python is definitely the way to go for longevity. Its community support is massive, and it’s the primary language for almost all new AI and Deep Learning breakthroughs.

   Answered 2025-03-20 by Michael Davis

• Michael is spot on. Especially with the explosion of Large Language Models, Python’s libraries like LangChain and Hugging Face have made it the indispensable language for the modern Data Scientist.

     Commented 2025-03-22 by Barbara Miller