Learn to clean, validate, and transform data for machine learning using Agentic tools with Polars, a fast, memory-efficient DataFrame library in Rust. You will take a raw, messy dataset and turn it into a documented, verified artifact: detecting missing values, inferring and checking schemas, catching drift between batches, applying declarative transformations, and building a bronze, silver and gold medallion pipeline that ends in Parquet.
Along the way you practice the habit that makes agentic development safe: the agent proposes, the compiler checks that it builds, and you verify the result by running it. Designed for learners new to Rust; no prior Rust experience required.
