32GB RAM vs faster CPU for data science: RAM wins for most workflows

Prioritize 32GB RAM over a faster CPU — you can’t compute on data that won’t load. Most data science tasks (pandas, numpy, scikit-learn) are memory-bound, not CPU-bound, and the difference between 16GB and 32GB is far more noticeable than i5 vs i7. Exceptions are GPU-based deep learning (where CPU is just a data feeder) or heavy CPU number crunching like Monte Carlo simulations. For typical 5-20GB file workflows, spend the extra CPU money on an SSD instead.