In this lecture, we'll cover:

• How Python will be used as a practical tool within the course
• Why coding proficiency develops gradually rather than being a binary skill
• The importance of learning programming through practice and projects
• What level of Python knowledge is (and is not) required for the course
• How we will build confidence using Jupyter notebooks to model structural behaviour

In this lecture, we explore how Python fits into the course and clarify what is expected in terms of programming ability. We reflect on the idea that coding is not a binary skill that we either possess or lack, but rather a continuum of proficiency. Writing code is fundamentally about expressing clear instructions, and everyone operates at a different stage of refinement and fluency. We emphasise that true progress in programming comes through practice, particularly by working on real projects rather than relying solely on tutorials.

We also clarify that the course will not include formal lectures on Python syntax or data structures. Instead, we will learn what we need organically as we use Python to model structural behaviour. The aim is not to turn us into professional Python developers, but to equip us with sufficient confidence and practical ability to use Python effectively in engineering study and work. By the end of the course, we will be comfortable using Jupyter notebooks to build simulations and will begin to recognise opportunities to apply Python more broadly in our own projects.

Next up:

In the next lecture, we get hands-on with installing Python via Anaconda and taking our first tour of the Jupyter Notebook environment.