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.
Joins and merges in plain English
Learn how pandas merges map to SQL joins, when row counts explode, and how to check keys with validate and indicator. A customers-and-orders walkthrough keeps grain honest before…
Models chooser: Haiku, Sonnet, Opus, and Fable (no hype)
Part 5 of the Claude product map: pick Haiku, Sonnet, Opus, or Fable without FOMO. Start on the default, upgrade only when a clear prompt still fails, and…
API and builders’ tools (light)
Part 4 closes the ChatGPT product map: ChatGPT Plus or Pro is not free unlimited API. Open platform.openai.com only if you build software that calls models. Most AMS…
Claude Design and Claude Science (where they fit)
Claude Design handles visual work (decks, mockups, prototypes, brand systems, exports). Claude Science is a research workbench with tools, compute, and auditable artifacts, not peer review. Part 4…
Aggregations and groupby
Learn pandas groupby as split-apply-combine, the same idea as SQL GROUP BY. Build sum, mean, count, and multi-metric aggregations, with a clear sales-by-region before and after table example.




