In this lecture, we'll cover:

• Installing Python using the Anaconda distribution
• Launching and navigating the Anaconda Navigator
• Opening and creating Jupyter Notebooks
• Understanding code cells and executing Python code
• Using Markdown and LaTeX within notebooks
• Saving, exporting and managing notebook files

In this lecture, we walk through how to install Python using the Anaconda distribution and explain why it serves as a convenient all-in-one package that includes Python, common libraries and the Jupyter development environment. We download the appropriate installer for our operating system, launch the Anaconda Navigator and open Jupyter Notebook through it. We also clarify how notebooks must be opened from within the Jupyter file navigation interface and how to create new Python 3 notebook files.

We then take a practical tour of the Jupyter Notebook interface. We learn how to work with code cells, execute Python code using Shift + Enter, rename and save notebooks, and export them in different formats. We also explore how to manage the notebook kernel, including restarting it if code hangs or crashes. A key focus is on the flexibility of notebooks: we can combine executable Python code with explanatory text, making them especially powerful for data analysis and modelling.

Finally, we introduce Markdown cells and demonstrate how they allow us to format text, create headers and embed images. We also highlight the ability to write mathematical expressions using LaTeX, making it far easier to typeset equations than in traditional word processors. Overall, we see how Jupyter Notebooks provide an interactive and highly visual environment that integrates code, text and mathematics in a single, coherent workspace.

Next up:

With our coding environment ready, the next lecture introduces Section 2, where we begin building the theoretical foundation by modelling element behaviour through stress–strain relationships.