• Data Integration: Importing data and connecting to multiple data sources (Excel, databases, cloud services).
• Data Transformation: Can use Power Query to clean and transform data.
• Data Visualisation: Create charts, graphs, and maps (also interactive features).
• Collaboration and Sharing: Publish reports and dashboards for sharing with others.

• Ease of Use: Drag-and-drop interface suitable for beginners.
• Employers Use It: Very common tool for visualisation in business analytics.
• Reporting: It’s highly useful for automatic and repeated reporting.
• Real-Time Analytics: Allows real-time data updates for timely insights.

• Excel
• Tableau
• Looker
• Domo
• SAP Analytics Clouds
• Coding langauges: R and Phython

And many more …

We can’t teach them all, so we aim to give you transferable foundations.

• Tourism Australia Which country has the most flights each week into Melbourne Airport? NZ

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• Recycling Victoria How many tonnes of paper were collected from Monash City Council in 2022? 52% 5860t

• Simple and easy to understand.

• Each level of the categorical variable is represented as a bar.

• The length of the bar represents its numeric value.

• Ordering bars and providing clear annotation are often necessary.

• These plots are commonly used to compare different categories such as sales performance across regions.

• Particularly useful when there are limited number of levels for comparison.

• Simple to understand at a glance.

• The circle is divided into slices that represent a category’s proportion of the whole.

• Often used to show proportions where the sum of the sections equal to one

• For example, how different products contribute to total sales.

• It is most effective when used with a small number of categories.

• Caution: These charts are highly criticised. Humans are very bad at translating angles to values. Best to avoid them when you can.

• It is very closely related to pie charts.

• Therefore, suffers the same drawbacks as seen before.

• It is better to use them sparingly.

• Alternatively, we can use bar plots or lollipop plots.

• Useful to visualise large numbar of categories as the plotting area is used efficiently

• Also highly useful for hierarchical data

• It displays data as a set of nested rectangles.

• Each rectangle represents a category or subcategory within a larger data set.

• The area of each rectangle represents a quantitative value, such as sales, revenue, etc., with larger rectangles indicate larger values.

• Colours can be used to represent another variable (or dimension) such as a performance metric.

• In business analytics, these plots are often used to represent the relative proportion of financial metrics such as sales revenue or profit across various categories such as regions, products or departments.

• If we have many levels in the hierarchy (>2), it is recommended to build an interactive figure. For example, clicking on a upper level of the structure will reveal the next level and so on.

• Treemaps can be cluttered and hard to interpret if there are too many categories or subcategories with very small values.

• The area of the rectangle gives visual sense of the magnitude of the proportion. However, it can be difficult to pinpoint the exact value without labels or tooltips.

Once the map is drawn, we can

• colour each region (choropleth map)

• add points or bubbles (bubble map)

• reshape the region (cartogram)

• show the connection between several regions (connection map)

• Basic barplots can be extended to introduce a secondary categorical variable.

• The levels in the secondary variable divide each of the levels in the primary categorical variable. e.g. 1. Country of Birth and 2. Census Year

• If length of the bar represents the frequency (or count) of the primary variable, the secondary categorical variable divide each bar’s length into sub levels.

• We can create three different types of plots:

  • Stacked barplots

  • Clustered (grouped) barplots

  • Percentage stacked barplots

• Each bar in a standard barplot is divided into a number of sub-bars stacked end-to-end, each one corresponds to a level of the secondary categorical variable.

• It allows us to see the composition of the total value for each level

• An example is comparing total sales from different regions, but you may like to know what categories makes up those sales.

• Use the domain knowledge or context to determine which variable will be the primary categorical variable and which will be the secondary categorical variable.

• Bars are grouped by position for levels of the primary categorical variable.

• The colours indicate the levels of the secondary categorical variable wihtin each group.

• It is used to look at how

  • the secondary categorical variable changes within each level of the primary categorical variable (within group).

  • the primary categorical variable changes across levels of the secondary variable (between group).

• It is not suitable to compare totals across levels of individual categorical variables.

• A variation of stacked barplots.

• Each primary bar is scaled to have the same length.

• It makes each sub-bar a percentage contribution to the whole at each primary level.

• It allows us to perform a better analysis of the secondary groups’ relative distributions.

• Also known as faceted charts.

• It allows us to display multiple visualisations (or “panels”) of the same chart type

• Each panel represents a subset of data based on a specific category

• Good for comparing patterns across different groups while keeping the visual structure consistent.

• The dimension we use to break down the data should have a meaningful number of categories.

• (Where appropriate) Ensure that all charts within the small multiples grid use the same axis scales (for both the \(x\)- and \(y\)-axes).

• Use chart types that naturally lend themselves to small multiples, such as line charts, barplots, scatter plots, etc.

• Keep the colours, labels, and design consistent across all small multiples to make the comparison easier and visually intuitive.

• Line charts

• Area charts

• Scatter plots

• Bubble charts

• Ribbon charts

• Waterfall charts

• so on

• Boxplots

• Histograms

• Density plots

• Contour plots

• Heatmaps

• so on

• Custom visuals are particularly useful when we need a unique chart, graph, or visualization that is not available in the standard Power BI visual library.

• We can create custom visuals using R, Python, JavaScript and TypeScript.

• To create a custom visual in Power BI, we need the Power BI Visuals Tools (pbiviz), which provide a framework for building, testing, and packaging custom visuals.

• After creating the custom visual, you can import it into your Power BI reports.

• An overview of visualisation in Power BI

• Covered the standard types of visualisations available

• Comfortable choosing the right visualisation for the data

• Discussed best practices for effective visualisations in Power BI