GPU DataFrames with cuDF

Most data workflows run on CPUs, and that's fine until your dataset gets large enough that you spend more time waiting than thinking. cuDF gives you a DataFrame interface that looks almost identical to pandas but runs on a GPU, and on large datasets the speed difference is not incremental. This course starts with what cuDF actually is: where it fits in the RAPIDS ecosystem, how it relates to pandas, and why GPU memory and parallelism produce the gains they do. Then you move into the practical side. You'll see which pandas patterns carry over directly, where they don't (dtypes, host-device transfers, operations that don't parallelize cleanly), and how to avoid the gotchas that trip people up when they first switch. By the end, you'll know how to load data onto a GPU, run real transformations with cuDF, and decide when GPU DataFrames are the right tool and when they're not.