Learn how to use Tableau from scratch
We’re excited to have you here! You are just starting out. Or you’re looking to sharpen your skills. Our step-by-step tutorials are designed to guide you through every stage of your learning journey. Our goal is to make complex concepts simple, clear, and accessible for everyone.
Explore at your own pace, dive deep into topics, and don’t hesitate to revisit sections as needed. If you have any questions, we’re here to help!
Let’s get started on mastering new skills together!
📚 The “How To” in Tableau – Course Lessons
Lesson 1 – Introduction to Tableau Visualisation
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Lesson 2 – How to Navigate the Tableau Desktop Layout (Part 1)
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Lesson 3 – How to Navigate the Tableau Desktop Layout (Part 2)
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Lesson 4 – How to Build Your First Visualisation in Tableau
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Lesson 5 – How to Add Excel Data Sources and Create Relationships in Tableau
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Lesson 6 – How to Use Unrelated Excel Sheets as Data Sources in Tableau
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Part B – Using Tableau for Data Analytics
Why Tableau is more than a visualisation tool
Most people meet Tableau as a chart builder: drag a field, get a bar chart. That is only the surface. Underneath the drag-and-drop sits a genuine analytics engine — level-of-detail expressions, table calculations, percentile and statistical functions, and an Analytics pane full of models that most users never open.
Part B is about that second half of the product. Each lesson takes one established data analysis methodology — the kind of method you might assume needs Python or R — and performs it end to end in Tableau: the method itself, the calculations behind it, the traps that quietly produce a confident wrong answer, and how to read the result once you have it.
Every lesson is built on the same invented coffee retailer, BrightBrew, so you can follow each step and land on exactly the same numbers. By the end you will have answered eight practical business questions — who is about to churn, what gets bought together, which products really carry the revenue, and whether a number that moved means anything at all — without leaving Tableau.
That is the real argument for doing the analysis where your data already lives: it shortens the distance between a question and a defensible answer, and it keeps the workings visible to everyone who sees the dashboard.
