Visual Analytics (BSAN7208)

This assessment task is an individual report that draws upon several data sets in a specific business context to support one or more of the United Nations Sustainable Development Goals. In that context, the report is to present an investment recommendation supported by data analysis and data visualisation techniques. The assignment requires that the student use at least three public data sets, taken together as a whole, related to the identified business context. These three or more data sets are then analysed in support of the development of an investment recommendation that addresses one or more SDGs. You should write the report from the perspective of a business or a government entity considering such an investment.

The essay is to present an analysis and interpretation of the data using at least two different forms of data visualisation as identified in lectures. The essay is to provide a broad overview of the context of the problem, identify the sources of the data sets and their relevance to the analysis, present the analysis and interpret the results, and finally link the analysis to key supporting arguments for the investment recommendation made. You should state any assumptions you make about your analysis or recommendation.

Key to success with this assessment task is succinct analysis and strong links between the data visualisation and the key supporting arguments for the recommendation. The discussion is to be supported by citing research that supports the context, analysis, interpretation, and key arguments made in support of the recommendation.

REQUIREMENTS

Scope

This assessment task is an individual report that draws upon several data sets. Note that in this context, a data set is simply a ‘table’ of data. It might be a listing of invoices, or employees, or of visitor numbers. Students using a nominated open data set should use at least one other open data set not provided. The report is to present a management recommendation (or recommendations) supported by data analysis and data visualisation techniques.

The assignment requires that the student acquire at least three data sets. These data sets, taken together as a whole, relate to the identified context and the associated SDG related problem. Therefore, at least two data sets are to be analysed in support of the development of a management recommendation.

The report is to provide a broad overview of the context of the business, identify the sources of the data sets and their relevance to the analysis, present the analysis and interpret the results, and link the analysis to key supporting arguments for the recommendation made. The analysis is to support the development of a management recommendation for a course of action, in line with an evidence-based management culture.

The report is to present an analysis and interpretation of the selected data using at least three different types of data visualisation. Furthermore, at least two data sets should be evident in two different types of data visualisations presented. For example, a geo-spatial representation might have two data sets represented in it and a trend line might be used to relate two data sets to each other over time.

As part of this requirement, the report should refer to at least three (3) academic, quality, peer reviewed research papers that support the issues, analysis and recommendations outlined in the report.

The report should identify the sources of the data used.

Generative AI statement:

This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.

To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.

In this assessment, you will create an interactive visualisation prototype. It will involve the design and implementation of a set of interactive visualisations and provide a critical evaluation. 

The aims of this assignment are:

• to design a set of interactive visualisations to allow for effective visual data exploration and sense making,
• to provide a critical evaluation of your design

In Assignment 1, you created a set of visualisations that provide initial insight about the data and helped answer some of the questions that you are exploring.

Part 1: Visualisation and interaction design 

In the first phase of this section, you will reproduce at least two of the first assignment visualisations, and at least one new visualisation, using the ggplot2 library in R. The aim is to demonstrate your understanding of the principles of the Grammar of Graphics to reproduce a visualisation idiom and create a new visualisation based on requirements. The two visualisations that you must reproduce can slightly differ from the ones you created, but the resulting visualisation should be as close as possible to the original ones.

For the new visualisation(s) that you will create with ggplot2, you need to:

• Describe your use of ggplot2’s grammar of graphics (in terms of data, layer, scale, coordinate and facets)
• Explain how your design choices allow for efficient visualisation of the data you want to analyse (refer to guidelines and principles from Munzner, Bertin, Tufte etc…)

In the second phase of this section, you will bring interactivity to your visualisations. Your interactive visualisations should demonstrate at least three interactions (see Heer and Shneiderman’s taxonomy) and show how they help data exploration and sense-making in the report. You are encouraged to use the interactive libraries covered in Module 3.4.

In the third and last phase of this section, you will provide a short evaluation of your design. This evaluation should address issues such as quality of encodings, efficient use of data/ink ratio, and other considerations covered in Module 4.

Part 2: Report 

Students will prepare a major report (maximum 8 pages) based on their design and evaluation of their data visualisations. The report is free form but must contain:

The ggplot2 code of the reproduced visualisations and the new visualisation(s) (Phase 1)

Which interactions were implemented and how they help data exploration and sensemaking (Phase 2)

A summary of your data visualisation evaluation (Phase 3)

Finalised visualisations that justify the final investment recommendations.

Part 3: Presentation

Each student is asked to prepare and record a presentation of 15 minutes summarising their work and demonstrating key points using the interactivity they built into their visualisations. 

Students need to demonstrate the challenges they had in creating their visualisations and how they addressed them.

Students should also describe any limitations of their visualisations and analysis. Any future directions that would be needed to better analyse or communicate the data should also be described.

The key success factors in the presentation are communication (how well the key points of the investment were summarised for its stakeholders), issues identification, any assumptions made in identifying issues, and presentation style.

Generative AI statement:

This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.

To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.

This assessment will be completed as a group of 3-5. You will critically reflect on each group member’s report and prepare a group presentation to demonstrate how interactivity built into group members’ visualisations enhanced reporting.

Groups need to critique the challenges students had in creating their visualisations and how they addressed them.

Groups should also critique any limitations of students' visualisations and analysis. Any future directions that would be needed to better analyse or communicate the data should also be described.

The key success factors in the presentation are communication (how well the key points of the investment were summarised for its stakeholders), issues identification, any assumptions made in identifying issues, and presentation style.

Generative AI statement:

This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.

A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.

To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.