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.

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There will be a 75% summative report (max 1000 words, ILOs 1,2, 3 , 5) and a 25% engagement portfolio that will consist of a backwards engineering and peer review exercise (ILO 1, 2, 4, 5), and 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. Quizzes may allow multiple attempts to support consolidation of learning.

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

This course aims to:

■ Develop practical programming skills in R, building confidence in computational thinking and problem-solving through real-world data analysis and visualisation.

■ Enable students to structure projects and produce reproducible, professional-grade reports that communicate complex information to diverse audiences.

■ Equip students to clean, transform, and visualise data using tidyverse tools and pipelines, developing systematic approaches to evidence-based decision-making.

■ Support critical and ethical engagement with generative AI for code development, fostering responsible digital practice and self-regulated learning.

■ Foster effective communication of data insights and collaborative problem-solving through peer programming and structured peer review.

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

1. Process and transform real-world datasets using reproducible programming workflows in R, demonstrating systematic approaches to data management.

2. Design and produce data reports that clearly and ethically communicate findings to both technical and non-technical audiences through effective visualisation and narrative.

3. Apply data skills to address socially relevant questions, selecting and justifying appropriate analytical approaches with consideration of sustainability and impact.

4. Evaluate and improve code through structured peer review and collaboration, articulating how different approaches to the same problem reflect different analytical choices.

5. Reflect critically on personal skill development, including the ethical and effective use of generative AI, and the role of data literacy in informed decision-making.