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.
Should you use ChatGPT Work or regular Chat for your task?
Part 4 closes the ChatGPT Work tutorial. Use a decision table for when Work beats Chat, when Chat is enough, and which wrong doors to avoid. Then continue…
How to profile a messy data table before you clean it
Cleaning before you look is how silent bias sneaks into dashboards. Learn a lightweight profile of counts, nulls, ranges, and weird categories with SQL and Python so every…
How to decide which plugins and connectors to add to Claude Code
Part 7 of Claude Code tutorial. Map built-in tools, MCP connectors, and plugins that bundle skills, subagents, hooks, and settings. Climb the ladder from a plain task to…
How to check the results of a long, multi-step ChatGPT Work job
Part 3 of the ChatGPT Work tutorial. Run multi-step jobs as a goal-plan-act-checkpoint loop: review the plan, steer mid-run, check usage, and finish with a result checklist that…
What does bad data mean? How to name the problem so you can fix it
Bad data is a shrug, not a diagnosis. Learn five practical quality dimensions (completeness, accuracy, consistency, timeliness, uniqueness) with workplace examples so you name the failure mode before…




