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.
SQL Tutorial 3: Retrieving Data with SELECT
Learn to retrieve exactly the columns you need with SELECT, rename them with aliases, remove duplicate rows with DISTINCT, and cap result size with LIMIT. Stop shipping SELECT…
SQL Tutorial 2: Master the Core Commands (SELECT, INSERT, UPDATE, DELETE)
Master the four core SQL commands: SELECT to read, INSERT to add, UPDATE to change, and DELETE to remove. Learn the safety habits that keep practice useful and…
SQL Tutorial 1: Setting Up Your SQL Playground
Set up a low-drama SQL playground with SQLite first, then compare common engines for later work. Create the toy customers and orders tables in your head and on…
SQL for Beginners: What is SQL and Why You Should Learn It
SQL is how you ask a database for answers instead of scrolling forever. This intro explains tables, the toy customers and orders schema, why SQL still matters at…
What are large language models (LLMs), and what are they good for at work?
Large language models draft text by predicting tokens at scale. Plain definitions, analytics use cases, grounding with retrieval, and a review habit so polished answers do not become…
