Our tutorials are short, task-shaped walkthroughs — the kind you reach for mid-deadline, not a semester course. They follow the arc of a real data story: find it, get it, clean it, check it, show it.

Finding & getting data. Where public data hides — agency portals, FOIA and Colorado CORA requests, the Wayback Machine, and bulk downloads — and how to capture it before it moves. For public-records strategy we point to the Reporters Committee’s open-government guide.

Cleaning & validating. Hands-on guides for OpenRefine (clustering messy categories), pandas, and the R tidyverse — plus how to validate as you go so errors surface early, not in print.

Exploring & publishing. Turning a clean table into a finding with Datasette, then into a chart a general audience can read (see evaluated tooling for the accessibility checks we apply).

We lean on the field’s best open references rather than reinventing them — The Turing Way for reproducible, ethical data science, and the data-journalism community around NICAR.

Every walkthrough is written for the practitioner who has a deadline and a dataset, not a computer-science degree. Pair them with our data documentation guides and tooling & code, read the Dispatch for worked examples, or bring your dataset to the help desk.