Jupyter notebooks are interactive documents that combine code, text and data visualisation. They're widely used for exploratory data analysis, presenting computational results, and sharing research. But when should you use a notebook versus a traditional script? And how can you integrate them effectively with your existing programming environments?

In this talk, I will introduce Jupyter notebooks from the ground up, covering installation, basic usage, and best practices for creating shareable, reproducible research outputs. Using Python-based notebooks as an example, I'll show how to manage kernels and environments, use interactive widgets, and create clear, well-documented analyses. In the second half of the talk I'll cover more advanced topics including useful JupyterLab extensions (such as git integration), Jupyter Docker deployment, tools for automating notebook workflows and sharing notebooks, and Jupyter alternatives. Whether you're completely new to notebooks or already use them regularly, this talk will include practical tips for improving your notebook usage and delivery.