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Getting started

K Clough edited this page Sep 10, 2025 · 17 revisions

You should follow this section in the first week of the course, and you may need to refer to it in future weeks as a reference.

The main thing that you need to do is to make sure that you can run the python notebooks and get course materials from github or from the module page. This can be completed in your own time, or in the first lab.

There are several options for running the Jupyter notebooks, and you can pick whichever you feel most comfortable with. You can use your own laptop or the lab computers, but if using the lab computers, my strong recommendation is not to use their inbuilt python installation, as it is unreliable, and instead use the Google Colab option below.

Option 1: Using Google Colab (recommended if using the lab computers, and if you are new to python)

You can use this on your own laptop or the lab computers. Google Colab is a web based installation that allows you to run the Jupyter notebooks on Google servers. It is not very fast, but nothing we do in this course will be computationally heavy. You will just need to follow:

Option 2: Using Python on you own laptop (recommended if you want to get more experience with the command line)

You should work through the following sections:

Option 3: Using Python on you own laptop, and GitHub to get and store files (recommended if you want to get more experience with GitHub, but only if you are quite confident in the command line)

You should work through the following sections:

The key packages used in this course will be:

Python

The computer language chosen for this module is Python, more specifically Python 3. You should aim to work with Python version 3.8 or higher (and definitely not Python 2). The book A Beginner's Guide to Python 3 Programming by John Hunt is available for free at the library as an ebook.

Jupyter Notebook

All the lab exercises are written as Jupyter notebooks. A Jupyter notebook contains text boxes in markdown (the same format used for writing this wiki) and programme code in Python, and the code can be run in the notebook rather than on the command line. They are especially useful for sharing code and results.

git (optional)

The basic purpose of git is to allow you to regularly save all your changes to a code so you can go back and see where you screwed up when things don't work or revert to an earlier version that is functional and try again. In a collaboration, it allows you to see where other people screwed up, and blame them (GitHub really has a blame button). You can also essentially use it like a fancy version of dropbox, to store all your files related to a code project, along with documentation related to it in a wiki like this one.

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