Python
What it is: General-purpose language dominating data science. Pandas, NumPy, SciPy, scikit-learn ecosystem.
What It Does Best
Full data pipeline. Load, clean, analyze, model, deploy—all in one language.
Machine learning ready. Best ML libraries (TensorFlow, PyTorch, scikit-learn). Seamless workflow from analysis to ML.
Automation. Not just analysis—automate reports, schedule jobs, build APIs around your models.
Pricing
Free. Open source.
When to Use It
✅ Building end-to-end data pipelines
✅ Machine learning is part of workflow
✅ Need automation and scheduling
✅ Want one language for everything
When NOT to Use It
❌ Pure statistics (R has better packages)
❌ Quick ad-hoc analysis (Excel faster for simple tasks)
❌ Team doesn't code (GUI tools better)
Bottom line: The Swiss Army knife of data work. Not always the best at one thing, but good enough at everything. Industry standard for data science.