Students will not be directly assessed on their command of programming languages (allowing more beginner and advanced students to participate on the course), but rather on how they engage with the quantitative skills being taught in the collaborative coding environment and how they design the teaching of any quantitative skills through the tutorial that they will develop. Students will be able to develop a tutorial at whichever skill level they prefer, and they will be assessed on the ability of their tutorial to communicate a specific quantitative skill, rather than the complexity of that skill.

Maintenance of individual repository and peer feedback on other students work - 20 %

Each student will create and maintain a GitHub repository that will contain their data, workflows, code and data visualizations. Students will fork online tutorials such that each student will have a copy of the course tutorials within their own online repositories. Each student will have a private repository that only the student and instructors will have access to and there will also be a course-level repository that will be accessible to all students and instructors on the course where group work will occur and students will be encouraged to share their personal code after they have complete the individual challenges.

Student will be encouraged to provide feedback to fellow students on their coding challenges and GitHub repository content by post issues on fellow students’ code and to work collaboratively with students on particular challenges through their GitHub repositories. The feedback that students provide and the contributions that individuals make to group challenges will be assessed.

At the end of the course, students will be assessed on both how they have structured and maintained their private repository and how they have contributed to the course repository and provided feedback to other students. Their work and also their engagement with the course material will be assessed through the repositories engagement statistics and the nature and depth of those engagements (Figures 1 and 2).


Figure 1. Example of how engagement can be tracked in a GitHub repository.


Figure 2. Example of how contributions by different students can be tracked in a GitHub repository.

Weekly challenges (5% per challenge x 8 challenges) - 40%

Students will be assessed on eight challenges across the course. Half will be individual challenges and half will be group challenges that the course will need to work together to solve, but assessment will be for individual contributions. Groups will be asked to establish a project “contract” contained within the course repository in their group’s folder as a readme file. Group projects will include contributions from each individual student that can be clearly indicated in the structure of the repository files and code and the contributions that student’s make from their individual GitHub accounts. Assessments will be set on the Monday of the week at 12 pm and due on the Friday of the week at 12pm. Students will be encouraged to work on their challenges during their tutorials and through independent study. Challenges will present a problem or research question that can be answered using a dataset and some sort of workflow development, data manipulation, data visualisation or code development. Each challenge will match the quantitative skill being taught in the respective week.

Development of a new tutorial - 40%

The final assessment on the course will be for each student to develop their own tutorial for the Coding Club website. The tutorial can be teaching introductory, intermediate or advanced quantitative skills in any programming language. The tutorial will be assessed on the way it teaches the quantitative skill including how clearly it is written, how well it is organised and the creativity used. Students will have completed 10 tutorials by the end of the course and will have access to all previous Coding Club tutorials as models for what can be produced. They will be asked to produce a tutorial on a unique quantitative skill and they will be encouraged to develop this tutorial as the course progresses and will receive formative feedback from the staff instructor and tutor as they progress throughout the course. Students will also be encouraged to get peer feedback on their tutorial as they develop it. The final hand in will include a GitHub repository for the tutorial including a markdown document of the tutorial, code extracts and visualizations of the tutorial content.

Check out example tutorials created by the Data Science in EES 2018 cohort!

Intro to spatial analysis in R

By Maude Grenier


Keen to learn how to import, manipulate, visualise and analyse spatial data? Learn about rasters and working with them in R? If yes, then check out Maude’s tutorial!