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Data Cleaning and Visualisation in R and Tableau

School of Business

About this use case

Students work with raw business datasets to develop practical data analytics skills by cleaning and preparing data in R before creating interactive visualisations in Tableau. AI tools may be used to support coding and debugging, while students are responsible for evaluating AI-generated suggestions and producing a final written report containing their code, visualisations and interpretation of business insights.

Learning outcomes:

Compare different sources, types and structures of business data.
Critically analyse business trends and patterns using descriptive analytics methods.

The assessment was designed to develop authentic AI-supported data analytics capabilities by engaging students in the key stages of an AI workflow, which includes data preparation, analysis and visualisation by using industry-relevant tools and datasets.
1. Introduce students to data wrangling techniques using R (dplyr and tidyr).
2. Provide instruction on developing dashboards and visualisations using Tableau.
3. Explain appropriate use of AI tools, including how they can assist with coding and debugging.
4. Brief students on responsible and ethical AI use using tutorial activities and LearnHQ resources.
Students are able to:
- prepare and clean structured datasets;
- create meaningful data visualisations;
- critically interpret business insights from data;
- use AI tools responsibly to support technical problem-solving while evaluating AI-generated outputs.

Try it out

This use case requires a complex level of effort to implement.

- Provide students with authentic raw datasets requiring preparation before analysis.
- Structure the assessment so students use R for data cleaning and Tableau for visualisation, with AI tools permitted as support for coding or debugging.
- Require submission of both technical artefacts and a written explanation demonstrating analytical reasoning.
- Clearly communicate expectations for AI use
- Distinguish between acceptable AI-assisted support and the student's own analytical responsibility.
- Reinforce ethical AI practices through tutorials and institutional guidance before students begin the assessment.
Indirect observation through drafts and documentation
Students submit their R code, Tableau visualisations and written interpretation of the results. These artefacts make the analytical process visible and allow the educator to assess how students clean and prepare data, develop visualisations, and critically interpret business insights. Where AI is used for coding or debugging, students remain responsible for evaluating the suggested outputs and demonstrating their own analytical reasoning.

Supporting resources

Here are some additional resources that you can browse to help you from TeachHQ:

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Sidebar

Unit: ETM5950	Condition: Individual
Class size: 130–140	Typology: Written
AI approach: Evaluating AI output	AI statement: Guided use
Visibility of Learning: Indirect observation through drafts and documentation

About the educator

Validated by

Sri Bala Murugan, Education Designer, Education Excellence