Category: Tutorials
You are in the right place if you want a lesson you can follow, not a glossary and not a product homepage. This shelf holds most of the articles on Analytics Made Simple. Two paths sit inside it on purpose. SQL is the database path: you have rows, and you need to ask a question without exporting another spreadsheet. AI is the tool path: chat products, open-weight models, and the jobs those tools are actually good at. Other tutorials land here when they teach a skill that does not belong on a narrower shelf.
A category page is the pile. A series page is the path, with a place to start and an order. If you open a tag or page 2 of this list, you are in a filing cabinet. The lesson is the article, or the series hub that lines the articles up.
Where to start: if the pain is a table, open the SQL series and use the first part on that page. If the pain is a model name or a chat tool, open the AI category and pick the product you already have on screen. If you are not sure which job you have, open Learn and choose the track that matches the work, not the logo.
Studying and explaining hard topics simply
Part 4 of the ChatGPT everyday tutorial. Study hard topics with explain-simply loops, quizzes, teach-back, Study Mode ideas, and the mistakes that turn “learning” into copy-paste theater.
Export results and hand off
Finish the job with clean CSV exports, optional Parquet or SQL loads, and short metadata notes for every file. Learn what each destination needs so BI tools and…
Talking to a codebase: ask, explore, change
Part 2 of the Claude Code tutorial. Talk to a repo in three beats: ask for a map with real paths, explore until you know what might break,…
Long docs and multi-file work
Part 3 of the ChatGPT everyday tutorial. How to handle long documents and multi-file work with chunking, indexing, quote checks, Projects, and a no-secrets recipe you can reuse…
A light cleaning pipeline
Turn ad-hoc notebook cells into a light load-clean-validate-summarize pipeline you can trust. Small functions, row-count checks, and cold-start reruns make your Python analytics reproducible for teammates.
