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docs/lectures/infovis/01_intro.md

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+# Fundamentals of Information Visualisation
+
+###### How to make use of data?
+
+- How do we avoid being overwhelmed?
+- How do we make sense of the data?
+- How do we harness this data in decision-making process?
+
+###### Objective
+
+- Transform the data into information (understanding & insight)
+
+##### What is Information Visualisation
+
+> - “… finding the artificial memory that best supports our natural means of perception.” - Bertin 1967
+> - “The use of computer -generated, interactive, visual representations opf data to amplify cognition” - Card, Mackinlay & Shneiderman 1999
+
+### Anscombe’s Quartet
+
+![1645029722.png](img/1645029722.png)
+
+Here we can see that statistically these sets are similar
+
+![1645029788.png](img/1645029788.png)
+
+However graphing them, we can see that these data sets are very different.
+
+#### Common Information Visualisations
+
+- Pie charts
+  - Very common, easy to understand, visually appealing
+  - Can make comparisons harder with many segments
+- Bar chart
+  - Makes comparisons between bars easier
+- Calendar View
+  - https://observablehq.com/@d3/calendar
+  - Can spot long-term trends - e.g. seasonal, annual trends
+
+###### Wikipedia Edit Evolution
+
+![img](img/a.png)
+
+Note the use of colour and shape.
+
+
+
+
+

docs/lectures/infovis/02_value_of_visualisation.md

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+# The Value of Visualisation
+
+###### Why create a Visualisation?
+
+- Answer questions (or discover them)
+- Make decisions
+- See data in context
+- Expand memory
+- Support graphical calculation
+- Find patterns and trends
+- Present arguments or tell a story
+
+> A picture is worth a 1000 words
+
+**Record** information
+
+- Blueprints, photographs, seimographs
+
+**Communicate** information to others
+
+- Share and persuade
+  - Think Florence Nightingale using a graph to show deaths to infection was the leading cause of death in hospitals
+- Collaborate and revise
+  - Think the London tube map, before was geographically accurate, now is only topologically accurate
+
+Analysis data to **support reasoning**
+
+- Find patterns
+  - Think the London Cholera map, how John Snow found out where the infection was coming from
+- Discover errors in data
+- Expand memory
+  - Imaging doing a sum like $34\times 52$ mentally verses with a pen and paper
+  - Visualising the sum (column multiplication) can expand your memory
+- Develop and assess hypotheses
+
+#### Different Stages of Visualisation
+
+![1645033524.png](img/1645033524.png)
+
+- Data transformation
+  - Create a structural model, schema, mapping raw data into data tables
+- Visual Mapping
+  - Create a visual spatial model, transforming data tables into visual structures
+- View Transformations
+  - Create views of the Visual Structures by specifying graphical parameters such as position, scaling and clipping.
+
+![1645033689.png](img/1645033689.png)
+
+###### Acquire
+
+- Obtain the data, whether from a file on a disk or network
+
+###### Parse
+
+- Provide some structure for the data’s meaning, and order it into categories
+
+###### Filter
+
+- Remove all but interesting data
+
+###### Mine
+
+- Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context
+  - Work out mean, standard deviation etc
+
+###### Represent
+
+- Choose a visual model 
+  - Bar chart, graph, pie chart etc
+
+###### Refine
+
+- Improve the basic representation to make it clearer and more engaging
+
+###### Interact
+
+- Add methods for manipulating the data or controlling what features are visible
+
+##### Interaction is Vital for Exploration
+
+- Engage in a dialog with your data
+- Employ interaction in a more fundamental manner to strengthen the power of visualisation
+