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.
What you get in the full ETF5922 Exam Bible
- 11 chapters, 44 pages — every examinable topic
- 29 original practice questions, each with a model answer in the Final exam format
- 11 worked examples, solved the way ETF5922 marks
- 22 computed figures, redrawn from the course’s own numbers
- Exam-morning plan for the Final exam (50%) — what to revise last, where marks are lost
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full exam bible.
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 other students are asking about ETF5922
- Where can I find lecture notes or study guides for ETF5922 at Monash?
- The ETF5922 study guide in AskSia Library covers Data visualisation and communication in 11 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in ETF5922, and how much is it worth?
- Yes. In the offering this AskSia Library guide is built from, Final exam carries 50% of the ETF5922 mark; the full split is Quizzes 10%, Assignment 1 10%, Assignment 2 15%, Assignment 3 15%, Final exam 50%.
- What does ETF5922 cover on power bi foundations?
- In AskSia Library's ETF5922 guide, the Power BI Foundations chapter explains field types, relationships, aggregation, measures, coordinated report pages, visible filter context and auditable dashboard comparisons.
- What does ETF5922 cover on r foundations and reproducible projects?
- In AskSia Library's ETF5922 guide, the R Foundations and Reproducible Projects chapter explains atomic vectors, missing values, factors, data frames, packages, pipes, project paths and clean-session reproducibility.
- What does ETF5922 cover on grammar of graphics and basic plots?
- In AskSia Library's ETF5922 guide, the Grammar of Graphics and Basic Plots chapter explains data, aesthetic mappings, geometric layers, bar charts, histograms, density plots, rugs, boxplots and tuning choices.
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 explanationExploration before explanation
Exploration is used to discover important features in data, whereas communication presents an understanding to other people.
- Exploration is used to discover important features in data, whereas communication presents an understanding to other people.
- A manager, technical analyst and public reader may need different levels of detail from the same dataset.
- Titles, ordering and annotations should reveal the question instead of merely decorating a finished plot.
- A pattern can be described from a display without treating it as proof of a cause.
- Use exploration and communication to frame the reader's task
- Check audience against decision before styling
Power BI Foundations
Field types, relationships, aggregation, measures, coordinated report pages, visible filter context and auditable dashboard comparisonsFilters define the number
Power BI appears in the published teaching sequence as an introductory visualisation tool.
- Power BI appears in the published teaching sequence as an introductory visualisation tool.
- Field types affect ordering, aggregation and the visual forms available to a report.
- A measure is evaluated under active filter context, while a stored column retains row-level values.
- Slicer state should remain visible when a report is exported or discussed outside the interactive page.
- Use field and relationship to frame the reader's task
- Check measure against column before styling
R Foundations and Reproducible Projects
Atomic vectors, missing values, factors, data frames, packages, pipes, project paths and clean-session reproducibilityObjects need declared origins
Atomic vectors store one basic type, and logical expressions can select or modify their elements.
- Atomic vectors store one basic type, and logical expressions can select or modify their elements.
- R uses NA for unavailable values, so summaries require an explicit missing-value policy.
- Factors encode categorical levels, while ordered factors permit ordinal comparisons.
- Projects, relative paths and clean-session execution make an R analysis reproducible on another computer.
- Use vector and numeric to frame the reader's task
- Check character against logical before styling
Grammar of Graphics and Basic Plots
Data, aesthetic mappings, geometric layers, bar charts, histograms, density plots, rugs, boxplots and tuning choicesMappings create the visual sentence
The grammar of graphics separates data, aesthetic mappings and geometric layers.
- The grammar of graphics separates data, aesthetic mappings and geometric layers.
- Putting colour inside aes maps a variable, while setting colour in a geom gives every mark the same appearance.
- Histogram bin width and density bandwidth change how local structure appears.
- Rugs preserve observation locations and boxplots compress centre, spread and potential outliers.
- Use dataset and aesthetic to frame the reader's task
- Check mapping against geom before styling
Data Wrangling for Visualisation
Selecting, filtering, arranging, mutating, grouping, summarising, reshaping and joining data without losing row meaningRows carry analytical meaning
Select changes columns, filter changes the retained population, and mutate creates variables.
- Select changes columns, filter changes the retained population, and mutate creates variables.
- Grouping followed by summarise changes the unit represented by one output row.
- Pivoting changes layout between wide and long forms without changing the substantive measurements.
- Joins can discard unmatched keys or multiply rows when the matching key is not unique.
- Use select and filter to frame the reader's task
- Check arrange against mutate before styling
Multivariable Encodings and Small Multiples
Colour, size, labels, accessible palettes, facets, pairs plots, parallel coordinates and coordinated plot layoutsChannels have unequal precision
Position carries the two primary quantitative variables in a scatter plot.
- Position carries the two primary quantitative variables in a scatter plot.
- Qualitative, sequential and diverging palettes match different variable semantics.
- Facet wrap repeats a plot by one category, while facet grid organises panels by row and column variables.
- Fixed facet scales support magnitude comparison; free scales expose local shape at the cost of direct comparison.
- Use position and colour to frame the reader's task
- Check size against label before styling
Graphical Excellence and Human Perception
Data density, data-to-ink ratio, chartjunk, efficient encodings, pre-attentive attributes, Gestalt grouping and misleading geometryGeometry can distort quantity
Data density evaluates how much useful information a display carries relative to its footprint.
- Data density evaluates how much useful information a display carries relative to its footprint.
- The data-to-ink principle removes non-essential decoration without deleting necessary context.
- Position and length support more precise judgments than angle, area or colour hue.
- Gestalt similarity, enclosure, connection and proximity shape the groups a viewer perceives.
- Use density and ink to frame the reader's task
- Check chartjunk against position before styling
Iterative Visual Design
Question-led drafting, analytical critique, controlled revision, validation and evidence-based evaluation of chart effectivenessCritique must predict a consequence
Iteration begins with a question and a testable draft rather than a polished final style.
- Iteration begins with a question and a testable draft rather than a polished final style.
- Analytical critique connects a visible choice to a consequence for accuracy or understanding.
- Changing one cause at a time preserves the link between a diagnosis and its repair.
- Validation checks values again and asks a fresh reader to recover the intended comparison.
- Use question and draft to frame the reader's task
- Check critique against accuracy before styling
Exploring Patterns and Uncertainty
Distribution, association, clusters, outliers, missingness, uncertainty displays and claims that remain proportionate to the dataPatterns need support and spread
Exploration looks for distribution, association, clusters, unusual observations and gaps.
- Exploration looks for distribution, association, clusters, unusual observations and gaps.
- A visible association is not evidence that one variable causes another.
- Missingness can differ by group, so completeness belongs beside a summary.
- Intervals and sensitivity views show how estimates vary across samples or analytical choices.
- Use distribution and association to frame the reader's task
- Check cluster against outlier before styling
Time Series Visualisation
Ordered observations, interval choice, gaps, trend, seasonality, events, aggregation, indexing and honest temporal comparisonTime order is part of the data
Time series observations must be ordered and interpreted at their actual interval.
- Time series observations must be ordered and interpreted at their actual interval.
- A line implies continuity, so missing periods should remain visible rather than being bridged silently.
- Trend, seasonality, cycles, events and level shifts are distinct temporal structures.
- Aggregation and moving summaries can reduce noise while concealing short-lived peaks.
- Use timestamp and interval to frame the reader's task
- Check chronology against trend before styling
Interactivity, Animation and Storytelling
Filters, tooltips, zoom, stable animation, static fallbacks, narrative sequence and annotations that follow rather than force the evidenceInteraction exposes hidden states
Interactive controls are useful when they answer a reader question that one static view cannot answer efficiently.
- Interactive controls are useful when they answer a reader question that one static view cannot answer efficiently.
- Filters must expose their active state, and essential values should not exist only in tooltips.
- Animation needs stable scales, positions and timing so apparent motion represents data change.
- A static fallback preserves the evidence for print, screenshots, accessibility tools and non-interactive contexts.
- Use interaction and filter to frame the reader's task
- Check tooltip against zoom before styling
The 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.
Worked example · free
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.
Questions students ask in each ETF5922 chapter
- When should an exploratory plot become a communication graphic?
- Rebuild the display when another reader must understand it without the analyst present. A communication graphic needs a declared purpose, stable labels, selected evidence and enough context to support the intended decision. Chapter 01 · Visualisation Purpose and Audience
- Why must a dashboard show an active slicer?
- A visible slicer tells the reader which subset produced every card and chart. Without that state, two viewers can read different totals from the same report and neither can reconstruct the comparison. Chapter 02 · Power BI Foundations
- Why can a clean-session failure reveal hidden state?
- A script that works only after earlier console actions depends on objects or packages it never creates. Restarting R exposes that hidden dependency and shows exactly what the reproducible workflow must declare. Chapter 03 · R Foundations and Reproducible Projects
- How do bin width and bandwidth alter a distribution?
- Bin width changes which observations are pooled into histogram bars, while bandwidth changes the smoothing of a density estimate. Both can reveal or conceal structure, so conclusions should survive several defensible settings. Chapter 04 · Grammar of Graphics and Basic Plots
- Why can a left join increase the row count?
- A left join multiplies rows when a key on the right has more than one match. Checking key uniqueness before plotting prevents duplicated matches from inflating totals or changing the apparent distribution. Chapter 05 · Data Wrangling for Visualisation
- When should facet scales remain fixed?
- Keep facet scales fixed when the reader must compare magnitudes across panels. Free scales can reveal within-panel shape, but they remove the common visual ruler and therefore change the question being answered. Chapter 06 · Multivariable Encodings and Small Multiples
- How does a two-dimensional icon exaggerate change?
- Scaling both height and width makes area grow with the square of the linear factor. A doubled icon therefore occupies four times the area and can imply a much larger change than the data contain. Chapter 07 · Graphical Excellence and Human Perception
- What makes chart critique analytical?
- Analytical critique connects a visible design choice to a consequence for accuracy, comparison, readability or audience understanding. It then proposes a targeted repair whose effect can be checked against the original. Chapter 08 · Iterative Visual Design
- Why should completeness sit beside a group mean?
- A mean can look stable even when many observations are absent. Showing the observed count or missing proportion reveals whether groups have comparable support and prevents precision from being inferred from incomplete data. Chapter 09 · Exploring Patterns and Uncertainty
- Why should a missing month remain a gap?
- Connecting across an absent month visually invents continuity and can hide collection failure. A visible break preserves the distinction between an observed change and a period for which no value exists. Chapter 10 · Time Series Visualisation
- When is animation better than aligned static frames?
- Animation helps when continuity of movement or state transition is itself the subject and stable reference points remain visible. Static small multiples are better when readers need precise comparisons across several moments. Chapter 11 · Interactivity, Animation and Storytelling
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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