Course Catalogue

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Applied Data Skills empowers students to interact with real-world datasets to generate meaningful insights and communicate them effectively. Working with sustainability-focused data, students will disrupt traditional learning through hands-on coding in R, collaborative problem-solving, and the ethical use of generative AI. The course enhances data literacy by combining technical training with reflection on researcher bias and reproducibility. Learners apply their skills through reproducible reports that address local and global challenges, becoming confident data storytellers and agents of change.

One two-hour workshop and one one-hour workshop each week for a total of 9 weeks, with a break for reading week and an additional drop-in session at the end of the semester to support the assessment.

None

There will be a 75% summative report (max 1000 words, ILOs 1-5) and a 25% engagement portfolio that will consist of a backwards engineering and peer review exercise (ILO 1, 2, 5), and nine weekly small-stakes quizzes (ILO 5).  

Reflection will be encouraged and supported through:

■ The summative report requiring students to explain the rationale for their choices.

■ The peer review requiring students to comment on how their peers' approach to coding differs from their own.

■ The weekly quizzes providing two attempts. The first attempt is to encourage reflection on the student's current understanding of the concepts learned that week with the second attempt provided to support consolidation.

Attendance at 75% of workshops will be required to be awarded for the engagement portfolio (extenuating circumstances and disability adjustments notwithstanding).

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Reassessment is only available for the 75% report. This is because the weekly MCQs are intended to consolidate learning as the course is progressing and therefore would be of little use in the reassessment period. Similarly, the coding exercises that involves peer review are also not reassessable because the purpose to iteratively support the final assessment and to engage in peer support which will not be possible in the reassessment period.

This course aims to:

■ Develop practical programming skills in R for real-world data analysis and visualisation.

■ Enable students to structure projects and produce reproducible, professional-grade reports.

■ Equip students to clean, transform, and visualise data using tidyverse tools and pipelines.

■ Support critical and ethical engagement with generative AI for code development.

■ Foster effective communication of data insights and collaboration through peer programming.

By the end of this course, students will be able to:

1. Import, clean, and manipulate real-world datasets using R programming.

2. Create fully reproducible data reports, demonstrating clear and ethical documentation of data processing and results.

3. Effectively visualise data to communicate findings to both technical and non-technical audiences.

4. Apply data skills to address socially relevant questions, with consideration of sustainability and impact.

5. Reflect on personal skill development and the role of data in driving informed decisions.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.