This article argues that Clojure may rival Python in the Data Science field due to its general-purpose nature, strong performance on the JVM, and rich library ecosystem. It highlights how Clojure's advantages address Python's limitations, particularly in speed and interop with native code.

Python data science workflows can be significantly accelerated using GPU-compatible libraries like cuDF, cuML, and cuGraph with minimal code changes. The article highlights seven drop-in replacements for popular Python libraries, demonstrating how to leverage GPU acceleration to enhance performance on large datasets without altering existing code.