Rewrite requirements, close #144

index.qmd

@@ -18,30 +18,21 @@ If you are feeling very adventurous, you could try using Julia or another langua
 
 # Prerequisites
 
-## General computing prerequisites
+## Hardware 
 
-You should have the latest stable release of R (4.3.0 or above) and be comfortable with computing in general, for example creating folders, moving files, and installing software.
-
-We recommend installing this software on a computer with decent resources (e.g. a laptop with 8 GB of RAM).
-
-## Data science experience prerequisites
-
-Prior experience of using R or Python (e.g. having used it for work, in previous degrees or having completed an online course) is essential.
-
-Students can demonstrate this by showing evidence that they have worked with R before, have completed an online course such as the first 4 sessions in the [RStudio Primers series](https://rstudio.cloud/learn/primers) or [DataCamp’s Free Introduction to R course](https://www.datacamp.com/courses/free-introduction-to-r).
-
-Evidence of substantial programming and data science experience in previous professional or academic work, in languages such as R or Python, also constitutes sufficient pre-requisite knowledge for the course.
+Access to a computer that you have permission to install software on, with at least 8 GB of RAM, is highly recommended.
+You could use a cloud-based service such as RStudio Cloud, Google Colab, or GitHub Codespaces, but you would need to be comfortable with using these services and would miss out on some of the benefits of using your own computer.
 
 ## Software
 
-Although you are free to use any software for the course, the emphasis on reproducibility means that popular popular and established data science languages R and Python are *highly* recommended.
+Although you are free to use any software for the course, the emphasis on reproducibility and interactive data science means that popular popular and established data science languages such as R and Python are recommended.
 
 The teaching will be delivered primarily in R, with some Python code snippets and examples.
 Unless you have a good reason to use Python, we recommend you use R for the course.
 
-### R software prerequisites
+### R
 
-For this module you therefore need to have up-to-date versions of R and RStudio installed on a computer you have access to:
+You should have the latest stable release of R (4.3.0 or above) and RStudio (recommended) or another IDE such as VS Code (if you have prior experience with it) installed on your computer.
 
 - R from [cran.r-project.org](https://cran.r-project.org/)
 - RStudio from [rstudio.com](https://rstudio.com/products/rstudio/download/#download) (recommended) or VS Code with the R extension installed.
@@ -62,6 +53,20 @@ Chapter 2 of Geocomputation with R (the Prerequisites section contains links for
 [project management section](https://csgillespie.github.io/efficientR/set-up.html#project-management).
 ]
 
-### Python software prerequisites
+### Python
+
+If you choose to use Python, you should be able to install it and manage your own Python environment, including installing packages and dealing with package conflicts.
+If you use Python we recommend using `pixi`, which can manage both R and Python environments.
 
-We installing Python with a modern package manager such as `pixi`.
+## Command-line experience
+
+You should be comfortable with computing in general, for example creating folders, moving files, and installing software.
+You should be comfortable with using command line interfaces such as PowerShell in Windows, Terminal in macOS, or the Linux shell.
+
+## Data science experience prerequisites
+
+Prior experience of using R or Python (e.g. having used it for work, in previous degrees or having completed an online course) is essential.
+
+Students can demonstrate this by showing evidence that they have worked with R before, have completed an online course such as the first 4 sessions in the [RStudio Primers series](https://rstudio.cloud/learn/primers) or [DataCamp’s Free Introduction to R course](https://www.datacamp.com/courses/free-introduction-to-r).
+
+Evidence of substantial programming and data science experience in previous professional or academic work, in languages such as R or Python, also constitutes sufficient pre-requisite knowledge for the course.