ETF5922 Data visualisation and communication
The Complete Exam Bible · S2 2026ETF5922 Overview
- Semester 2, 2026
- Postgraduate unit
- Monash University
- Power BI and R
- Individual final examination
What this unit teaches
Data visualisation and communication is a practical course about turning data into displays that help a reader compare, interpret and decide.
- Assessed by Three written assignments, quizzes and a final exam, with the published weights summing to one hundred per cent.
- Hardest step Choosing an encoding that makes the intended comparison easy without letting the visual overstate the evidence.
- How to prepare Rebuild a plot from a clean table, recompute one comparison and write a qualified interpretation after every practice session.
- Software route Power BI introduces report construction, while R and ggplot2 support reproducible wrangling, layered graphics and advanced displays.
How ETF5922 is assessed
| Component | Weight | Format |
|---|---|---|
| Quizzes | 10% | Individual, best 10 of 12 |
| Assignment 1 | 10% | Individual written task |
| Assignment 2 | 15% | Group plus individual written task |
| Assignment 3 | 15% | Individual written task |
| Final exam | 50% | Individual examination |
The 2026 handbook groups assessment as Written 40%, Quiz or Test 10% and Examination 50%. The unit LMS gives the matching component split: Assignment 1 at 10%, Assignment 2 at 15%, Assignment 3 at 15%, quizzes at 10% and the final exam at 50%. The three assignments sum to the handbook's written category. The unit material directs students to the Handbook for hurdle requirements; no component hurdle is asserted here.
How the published marks divide
The segment widths reproduce the LMS weights in percentage points and sum to one hundred.
What ETF5922 covers
Eleven chapters follow the published teaching sequence from visual purpose and Power BI through R, wrangling, graphical excellence, time series, interactivity and animation.
Visualisation Purpose and Audience
Exploration versus communication, audience decisions, analytical purpose and the boundary between a visible pattern and an unsupported explanation02Power BI Foundations
Field types, relationships, aggregation, measures, coordinated report pages, visible filter context and auditable dashboard comparisons03R Foundations and Reproducible Projects
Atomic vectors, missing values, factors, data frames, packages, pipes, project paths and clean-session reproducibility04Grammar of Graphics and Basic Plots
Data, aesthetic mappings, geometric layers, bar charts, histograms, density plots, rugs, boxplots and tuning choices05Data Wrangling for Visualisation
Selecting, filtering, arranging, mutating, grouping, summarising, reshaping and joining data without losing row meaning06Multivariable Encodings and Small Multiples
Colour, size, labels, accessible palettes, facets, pairs plots, parallel coordinates and coordinated plot layouts07Graphical Excellence and Human Perception
Data density, data-to-ink ratio, chartjunk, efficient encodings, pre-attentive attributes, Gestalt grouping and misleading geometry08Iterative Visual Design
Question-led drafting, analytical critique, controlled revision, validation and evidence-based evaluation of chart effectiveness09Exploring Patterns and Uncertainty
Distribution, association, clusters, outliers, missingness, uncertainty displays and claims that remain proportionate to the data10Time Series Visualisation
Ordered observations, interval choice, gaps, trend, seasonality, events, aggregation, indexing and honest temporal comparison11Interactivity, Animation and Storytelling
Filters, tooltips, zoom, stable animation, static fallbacks, narrative sequence and annotations that follow rather than force the evidenceThe published teaching sequence begins with the purpose of visualisation and an introduction to Power BI, then moves into R, the grammar of graphics, data wrangling and plots involving one, two or more variables. The later weeks add principles of graphical excellence, iterative improvement, pattern exploration, time series, interactivity and animation.
This order matters because a visual argument is only as reliable as the table, transformation and encoding beneath it.
Exploration and communication are different jobs
An exploratory plot helps an analyst discover structure. It can be provisional, crowded and rapidly revised because the person reading it also knows how it was made. A communication plot has to work for someone else.
It needs a stated purpose, a visible comparison, readable labels, an appropriate scale and enough context to prevent an attractive pattern from becoming an exaggerated claim.
Across the unit, the strongest answers connect audience, purpose, data and design rather than treating chart choice as a menu of software options.
Power BI and R play complementary roles
Power BI is introduced as a report-building environment in which field types, relationships, measures, aggregation and filter context determine what a visual means.
R then provides a programmable route through objects, vectors, factors, data frames, packages, projects and pipes. The reproducibility lesson is central: code should run from a clean session, use stable project paths and make each transformation inspectable.
A chart that depends on a hidden object or private file path cannot be checked by another analyst.
The grammar of graphics gives plots a common language
In ggplot2, data provide observations and variables, aesthetic mappings connect variables to visible properties, and geometric layers determine the marks. Scales, facets, coordinates and themes extend that core.
The distinction between mapping and setting is especially useful: a colour inside an aesthetic represents a variable, while a colour supplied directly to a geom gives every mark the same appearance.
Once this grammar is understood, chart construction becomes a sequence of explicit decisions rather than trial and error.
Wrangling is part of the visual argument
Selecting columns, filtering rows, creating variables, grouping, summarising, reshaping and joining tables can all change the population or level represented by the data. A grouped summary collapses many observations into one row per group.
A join can lose unmatched records or multiply rows when keys repeat. Missing values can alter denominators and comparisons. The unit therefore rewards checking row meaning and key structure before styling a plot.
A convincing visual with an inflated total is still wrong.
Graphical excellence is about evidence, not decoration
Good displays increase useful data density and reduce non-essential visual ink without removing context needed for honest interpretation. Position and aligned length usually support more accurate comparison than angle, area or hue.
Pre-attentive attributes guide attention, while Gestalt principles explain why similarity, enclosure, connection and proximity create perceived groups. These mechanisms should reflect the data structure.
If styling groups unrelated observations, the design communicates an incorrect model even when the values are technically accurate.
Iteration makes critique operational
Iteration starts with a question, produces a draft, diagnoses a specific mismatch and changes one cause at a time. Useful critique connects a visible choice to a consequence for accuracy, clarity, readability or effectiveness.
It is not enough to say that a colour is unattractive or a chart is confusing.
A strong critique explains, for example, that a legend requires repeated lookup, a free facet scale blocks magnitude comparison, or an alphabetical order hides ranking, then proposes a targeted repair and validates the revised display.
Advanced displays keep the same discipline
Pattern exploration, time series, interaction and animation extend the visual toolkit without changing the standard of evidence.
Associations do not establish causes. Missing periods should remain visible. Temporal aggregation can hide peaks. Filters must expose their active state. Tooltips should not hide evidence essential to the main claim. Animation needs stable scales and a static fallback. In every case, the display should help a reader recover the comparison and understand the uncertainty or limitation that remains.
Audit a visual claim with fresh numbers
- 2Latest weekly change: 95 - 79 = 16 cases.
- 2Relative weekly change: 16 / 79 = 20.3%.
- 2First-to-last change: 95 - 72 = 23 cases, or 31.9% relative to 72.
- 2Use a labelled line with points and state that completed cases increased; do not infer a cause from four observations.
Key terms
- Aesthetic mapping
- A rule connecting a data variable to a visible property such as position, colour, fill, size or shape.
- Data wrangling
- The work of selecting, filtering, transforming, summarising, reshaping and joining data before analysis or visualisation.
- Facet
- A small-multiple panel that repeats the same plot for levels of one or more categorical variables.
- Graphical excellence
- Visual communication that presents evidence clearly, accurately and efficiently without misleading geometry or unnecessary decoration.
- Filter context
- The active subset under which a report measure is evaluated, including slicers and other selections.
- Reproducibility
- The ability to recreate an analysis from its data, code, packages and documented steps in a clean environment.
- Time series
- Observations ordered by time, with an interval and calendar structure that shape valid comparisons.
ETF5922 FAQ
How is the unit assessed?
The published component split is quizzes 10%, Assignment 1 at 10%, Assignment 2 at 15%, Assignment 3 at 15% and a final exam at 50%. The handbook's broader categories reconcile with the same total.
Do I need both Power BI and R?
Yes. The published teaching sequence introduces Power BI before moving into R and ggplot2. Treat them as complementary routes for building, checking and communicating visual evidence.
How do I choose a chart type?
Start with the variable types and the reader's comparison. Use position and aligned length for precise comparisons, preserve ordered time, and add colour, size or facets only when they encode a necessary variable.
Why does data wrangling matter for a chart?
Filtering, grouping, reshaping and joining can change the population, denominator or row count. Audit those transformations before styling because the visual inherits every mistake in its table.
What should a visualisation interpretation include?
State the dominant pattern, support it with one computed comparison, explain the relevant encoding and name a limitation. Avoid causal language unless the study design supports it.
How to study for the exam
Build revision around short production cycles. Start with a small table and state what one row represents. Perform the needed wrangling, check row counts and missingness, then construct a plot from explicit data, mapping and geom choices. Recompute one comparison outside the chart so you can verify labels and denominators.
Finish with a three-sentence interpretation: visible pattern, quantified support and boundary of the claim. Alternate construction with diagnosis by repairing misleading scales, crowded legends, hidden filters, inaccessible palettes and unmatched join keys. For the exam, practise explaining why a choice helps the reader, not merely naming a software function.
Revisit the published assessment split when allocating time because the final exam carries half the unit mark.
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