I am a student looking to break into the AI field. I see a lot of academic papers using R for statistics, but almost all the job descriptions for "Machine Learning Engineer" or "AI Researcher" require Python. Is R still relevant for Deep Learning, or has Python's ecosystem with TensorFlow and PyTorch made it the only viable choice for the future?
3 answers
For Deep Learning specifically, Python is the undisputed leader. While R is fantastic for statistical analysis and beautiful visualizations (like ggplot2), the major AI frameworks—TensorFlow, PyTorch, Keras, and JAX—are all built with a "Python-first" mentality. This means new research papers almost always release their code in Python first. Furthermore, Python’s versatility as a general-purpose language allows you to take a model from a research script and deploy it into a production web app using FastAPI or a cloud service, which is much harder to do with R. If your goal is AI, invest 90% of your time in Python.
Are you interested more in the "Science" side (experimental design and p-values) or the "Engineering" side (building and deploying models)? R still has a slight edge in pure statistical theory, but Python is catching up even there with libraries like statsmodels.
Python has a much larger community and more job openings. If you look at LinkedIn, the ratio of Python AI jobs to R AI jobs is probably 10 to 1 right now.
Linda is spot on. I’ve been an AI recruiter for three years, and I can count on one hand how many times a client asked for R. It’s almost always Python and SQL these days.
Mark, I’m definitely leaning towards the engineering side. I want to build computer vision models for self-driving drones. Based on that, would you say that learning Python's OpenCV and PyTorch is more critical than learning the statistical foundations taught in R?