This course asks you to combine data, a visualisation and a narrative into something powerful enough to drive change. That is a genuinely useful skill, and it sits one small step away from a genuinely serious problem.
This course asks you to combine data, a visualisation and a narrative into something powerful enough to drive change. That is a genuinely useful skill, and it sits one small step away from a genuinely serious problem. The same techniques that make a chart persuasive can make it misleading, and the line between them is not marked. Students who treat the course as a software unit learn R and Tableau and produce competent work. Students who understand that persuasion carries an obligation produce work that is both better and defensible. Below is how MAAS mentors read a course built like this.
Author: MAAS Editorial Team · Reviewed by a Senior Analytics mentor (PhD, Statistics)
Last updated: 2026-08-12
Category: writing-tips
What is the course, and what does it actually cover?
Direct answer: At UNSW, COMM2501 is Data Visualisation and Communication, a 6 unit of credit undergraduate course in the Business School. It introduces statistical and visualisation tools for exploratory data analysis, teaches what makes a visualisation effective, and covers building interactive visualisations in R, Tableau and other tools, including immersive graphical techniques. It carries a stated strong focus on data storytelling, described as combining data, its visualisation and a narrative into a story that drives change.
Evidence: The course description opens by naming the problem it exists to solve: businesses generate enormous quantities of data, and the ability to give visual access to it is an essential analyst skill. Note the framing. Visualisation is presented as an access problem, not a decoration problem. The chart is the interface between a dataset and a decision.
Example: A student produced a dashboard with twelve charts covering everything in the dataset. It was thorough and nobody could use it. The revision showed three charts answering one question, and the analyst had made a judgement about what mattered rather than deferring it to the reader.
Why does "effective" have a technical meaning here?
Direct answer: Because effectiveness in visualisation has been studied experimentally rather than left to taste. There is a measured ordering of how accurately people extract quantitative information from different visual encodings, and it gives you a defensible reason to choose one chart over another.
Evidence: Cleveland and McGill (1984) set out to give graphical methods a scientific foundation, identifying a set of elementary perceptual tasks people perform when reading quantitative information off a graph and then ordering those tasks by how accurately people perform them. Their design principle follows directly: graphs should use tasks as high in that ordering as possible. Applying it, they concluded that popular forms including pie charts and divided bar charts needed what they called radical surgery, because a pie chart asks the reader to judge angles when the same data can always be shown on a common scale where the reader judges position instead.
Example: Asked why she replaced a pie chart with a dot chart, a weaker answer said pie charts are considered outdated. A stronger one said the data was a set of parts of a whole that could be placed on a common scale, that judging position along a common scale is more accurate than judging angle, and that the ordering of the five categories was unreadable in the pie and immediate in the dot chart. Same decision, and only one of them is an argument.
Where is the ethical line in data storytelling?
Direct answer: It sits at the point where the narrative starts selecting the evidence rather than following it. Storytelling asks you to have a point. It does not license you to arrange the data until it makes that point.
Evidence: Distortion is not hypothetical, and its effects have been measured. Pandey et al. (2015) ran an empirical analysis of common distortion techniques in visualisation, testing whether familiar manipulations actually change what readers conclude. The finding that matters for your assignments is that they do. A truncated axis, an inverted scale or a manipulated aspect ratio are not stylistic quirks that sophisticated readers automatically discount. They shift judgements.
That result cuts both ways, which is why it belongs in a storytelling course rather than only in an ethics one. If distortion works, then every design choice you make is a choice about how much the reader will believe, and you cannot claim neutrality for a decision you made deliberately.
Example: A student building a case about rising costs truncated the y-axis, which made a modest increase look dramatic. Challenged, she said the trend was real. It was. The revised chart kept the full axis, and she made the case in the annotation instead, pointing out that a four per cent rise on that base was material given the margin. The argument survived; it just moved from the axis to the sentence, where it could be checked.
What does the exploratory stage owe the final chart?
Direct answer: An honest account of what you removed. Exploratory analysis always involves decisions about missing values, outliers and time ranges, and those decisions shape the chart far more than the colour palette does.
Evidence: The course sequence puts exploration before communication for a reason. By the time you produce a final visualisation you have already chosen which rows to keep, how to treat gaps, and where the series starts and ends. A reader seeing only the finished chart cannot see any of that, which means the obligation to disclose sits with you rather than with them. This is the same principle as the axis question, applied earlier in the pipeline.
Example: A student excluded two outlier months as data errors and the trend became clean and convincing. When a tutor asked what had happened in those months, it turned out one was a genuine shock the story needed to explain rather than remove. The final version kept both points, annotated the shock, and made a stronger claim because it accounted for the thing that did not fit.
What separates a data story from a report?
| Element | A report does this | A data story does this |
|---|---|---|
| Structure | Covers the dataset | Answers one question |
| Chart selection | Shows what is available | Shows what carries the argument |
| Annotation | Labels axes | States what the reader should notice |
| Counter-evidence | Omits or buries it | Addresses the strongest opposing reading |
| Conclusion | Summarises findings | Says what should change and on what basis |
Evidence: The course description names the goal as a story powerful enough to drive change, which means the deliverable is judged as an argument. An argument that has not met its strongest objection is incomplete regardless of how well it is rendered.
Example: Two projects examined the same productivity dataset. The first presented the trend and concluded that productivity was the problem. The second presented the same trend, acknowledged the competing reading that focuses on job quality and underemployment, explained which parts of the data distinguish the two, and then argued for its conclusion. The second is the one that would survive a question from someone who disagreed.
How much should the tooling matter?
Direct answer: Less than students expect, and it should be invisible in the final work. R and Tableau are how you produce the artefact. Nothing about your grade depends on the reader being able to tell how hard it was.
Evidence: The course positions the tools inside a sequence that starts with exploratory analysis and ends with communication. Exploration is where the tool earns its keep, because that is where you are finding out what is in the data. By the time you are communicating, the question is whether the chart is the right chart, and that question has the same answer whichever software drew it.
Example: A student spent most of a project week building an interactive visualisation with linked filters. It worked, and no reader ever changed a filter. A static chart with a clear annotation would have carried the same insight, and the week would have gone into checking whether the insight was right.
A practical order of work
- Write the question before you open the data. A data story with no question becomes a tour of the dataset.
- Explore first and separately. Keep exploratory charts out of the final piece; they were built to inform you, not the reader.
- Choose encodings by perceptual task, not by variety. If the reader needs to compare values, give them position on a common scale.
- Write the annotation before you finalise the chart. If you cannot say what the reader should notice, the chart is not finished.
- Draw the honest version first. If the effect disappears at a full axis, that is a finding about the effect, not a problem with the axis.
- Name the strongest opposing reading of your data and address it. This is what converts a presentation into an argument.
Frequently asked questions
Do I need programming experience before this course?
The course teaches the tools, and students without a coding background do fine, but budget time in the first weeks for the mechanics so that the software stops competing with the thinking later on.
Is a more complex visualisation better?
No. Complexity is only justified when the question requires it. Interactivity in particular should be added because a reader needs to ask their own follow-up question, not because it is available.
Can I truncate an axis?
There are legitimate cases, and the test is whether the truncation helps the reader see something real or manufactures an impression the data does not support. If you truncate, say so in the chart and give the reader the base.
How do I handle a dataset that does not support my argument?
Change the argument. This sounds obvious and it is the single most common place where student data stories go wrong, because the narrative gets fixed before the analysis finishes.
Should I cite visualisation literature in an analytics assignment?
Follow your course outline. Where it is appropriate, a design choice justified by perceptual research is far stronger than one justified by preference, and it takes one sentence.
Where MAAS fits
MAAS mentors work alongside students on courses like this rather than in place of them. On a data story, the most useful review is usually a hostile reading: what would a person who disagreed with your conclusion say about this chart, this axis, this omitted year. Work that has survived that reading tends to be simpler than work that has not, because the decorative parts do not survive the question. The analysis stays yours. If that is useful, our academic support service and our data and coding projects service are the two places to start.
References
Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531–554. https://doi.org/10.1080/01621459.1984.10478080
Pandey, A. V., Rall, K., Satterthwaite, M. L., Nov, O., & Bertini, E. (2015). How deceptive are deceptive visualizations? An empirical analysis of common distortion techniques. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1469–1478). ACM. https://doi.org/10.1145/2702123.2702608
Tools & resources
UNSW Sydney. (2025). COMM2501: Data Visualisation and Communication. https://handbook.unsw.edu.au/undergraduate/courses/2025/COMM2501