What is COMM2501 Data Visualisation and Communication?

Direct answer: COMM2501 Data Visualisation and Communication is a 6 unit-of-credit undergraduate course run by the School of Risk and Actuarial Studies in UNSW Business School. The 2026 handbook lists it on the Sydney campus in Term 2 and Term 3, and the main task is a data story about a global challenge.

The 2026 handbook entry for COMM2501 states the aim in one sentence: "In this course, we will give you the tools to create your own data story addressing a pressing and current global challenge as identified by the UN SDGs" (UNSW Sydney, n.d.). It adds that you will learn "how to criticise misrepresentations of data (especially the intentional ones)" (UNSW Sydney, n.d.). Two skills therefore sit side by side: building a persuasive visual story and auditing other people's charts for distortion.

Dr Leung describes most of his class as studying actuarial or business analytics degrees, students strong on statistics who find "communicating to non-technical stakeholders can be quite challenging" (UNSW Business School EDI Team, 2024). The same news piece reports that the Business School made the course compulsory for Actuarial Studies undergraduates, although it is not an official requirement for degree accreditation. That description comes from his 2023 to 2024 teaching, so check your own program rules.

Source: UNSW Sydney handbook entry for COMM2501, 2026, read 2026-10-27.

Data visualisation is a field of its own, but at UNSW Sydney this course is assessed on the story you build.

Direct answer: The 2026 handbook sets a two-part prerequisite and lists two individual project tasks, Data Story Content and Data Story Project. The weighting field reads 0 for both tasks in the handbook data, so take the marks split from your official course outline before you plan your time.

The handbook states the enrolment rule as "Prerequisite: (ECON1203 or COMM1190 or MATH1151 or MATH1141 or MATH1131) and (COMM1822/COMM2822 or ACTL1101 or COMP3311)" (UNSW Sydney, n.d.). In plain terms you need one foundation statistics or maths course and one data or actuarial course. If COMM1190 is your route, our COMM1190 Data, Insights and Decisions guide covers the first half, and the COMM2822 databases guide covers one of the options in the second half.

Source: UNSW Sydney handbook, COMM2501 requisite and assessment fields, read 2026-10-27.

The five course learning outcomes shape how you should read those tasks. CLO1 asks you to "Compose analytics-informed narratives for actionable business recommendations and communicate them convincingly to technical and non-technical stakeholders" (UNSW Sydney, n.d.). CLO2 asks you to "Critically examine misrepresentations of data and apply relevant approaches to prevent and mitigate ethical risks" (UNSW Sydney, n.d.). CLO5 is the only outcome tied to the second task alone, and it concerns career aspirations.

Direct answer: Pick the goal you can explain in one honest sentence of personal interest, then narrow it to one problem you could act on within a three-minute talk. Choose a dataset only after the question is fixed, preferably from the official UN SDG reporting that covers over 200 countries and territories.

Dr Leung begins by asking students to "pick an SDG and simply explain why it interests them" (UNSW Business School EDI Team, 2024). He also says this "isn't a research project", because each student must "give a recommendation on how to tackle the problem, using the data they've found to validate that recommendation" (UNSW Business School EDI Team, 2024). In his 2023 to 2024 teaching the talk lasted three minutes: "You can't change the world in three minutes."

The Sustainable Development Goals are 17 goals inside the 2030 Agenda adopted by all United Nations Member States in 2015 (United Nations, n.d.). For data, the Sustainable Development Goals Report 2025, the tenth annual stocktaking, draws on data from over 200 countries and territories and more than 50 agencies (United Nations Department of Economic and Social Affairs, 2025). It also records universal electricity access in 45 countries and elimination of neglected tropical diseases in 54 countries.

Source: illustrative examples written by MAAS for this guide, not real data.

Each row narrows a goal until one audience could act on it.

Direct answer: Start bar-chart axes at zero, use one scale per chart, space ticks evenly and keep bins the same size. These five checks catch common distortions named in published research, and CLO2 expects you to examine misrepresentation.

Lo et al. (2022) collected 129,125 visualisation images, examined 6,500, found 1,979 candidate charts and tagged 1,143 with issues, which produced 74 types of issue. Truncated axis was the most frequent, with 171 tagged cases, and dual axis ranked fourth with 78. They warn that "when it is done poorly or maliciously, a visualization may become misleading or even deceiving" (Lo et al., 2022, p. 515). Even their coders found some cases undecidable, such as whether truncating a line chart's y-axis is valid, so explain your choice.

Tonglet et al. (2025) built a benchmark of 2,604 real-world charts covering 12 misleader types. They describe misleading charts as ones that "distort the underlying data, typically by violating design principles, leading readers to draw inaccurate conclusions" (Tonglet et al., 2025, p. 1).

Source: definitions from Tonglet et al. (2025, pp. 3 to 4), made into a checklist.

Tonglet et al. (2025) define the first as "an axis does not start from zero, thus exaggerating differences between values" (p. 3). Add one line under each final chart naming the checks it passed.

Direct answer: Name one audience, set the scene with the problem's size, show only the data that proves the point, then end on a recommendation. Dr Leung calls scene-setting the first rule of data storytelling, and CLO1 asks you to persuade non-technical stakeholders as well as technical ones.

Dr Leung names the common failure: "A common mistake that students or early grads make when presenting their data is just saying, 'Here's all the data I have, here's the model I used' etc." (UNSW Business School EDI Team, 2024). His fix starts with a rule: "Number one in data storytelling is how you set the scene" (UNSW Business School EDI Team, 2024). Senior stakeholders, he says, "want your key insights, a summary of why this data is worth talking about."

A workable running order for a three-minute talk is below.

1. Scene: one sentence that names the audience and the problem.
2. Size: one number on how many people are affected, with its source.
3. Evidence: one or two charts, each with a title that states the finding.
4. Recommendation: one action the audience can take.
5. Limit: one line on what the data cannot show.

The United Nations Statistics Division offers an open toolkit that "aims to support countries in data storytelling through infographics, reports, online platforms, presentations, promotion materials and social media" (United Nations Statistics Division, n.d.). Its templates are free, which makes it a practical layout reference.

Treat the second task as a portfolio piece: one student in the UNSW news piece says a recruiter "had viewed my data story project on LinkedIn" (UNSW Business School EDI Team, 2024). For delivery practice, see how presentation rubrics are graded and presenting without reading your slides.