What you'll build

You'll work with Thistlewood Goods, a small home-goods retailer, building one real analytics pipeline that grows module by module: reading and summarizing raw CSVs, mastering Polars' expression system (the foundation everything else builds on), cleaning and validating messy real-world data, wrangling strings and categories, working with nested list and struct columns, aggregating with group_by and window functions, joining and reshaping across five real tables, analyzing time series, and finally moving from eager to lazy execution across CSV, JSON, Parquet, and SQLite. The capstone assembles all of it into one local analytics pipeline — ingest, clean, join, aggregate, and write processed output — entirely on your own machine.

Before you start

You'll need basic Python — variables, functions, lists and dictionaries — and a working command line. No pandas experience is required: this course teaches Polars as its own complete tool, not a pandas port. No cloud account and no GPU are needed anywhere in this course.

Set up your environment

Everything in this course runs for real, locally, on your own machine — no cloud account, nothing to pay for.

1. Python 3.10+ and a virtual environment — Module 1 walks through project setup from scratch.
2. Polars — a single pip install polars, verified in Lesson 1.

Every command in every lesson is real and was actually run to produce the output you'll see — nothing is simulated or invented.