Tidy up

docs/lectures/infovis/01_intro.md

@@ -4,7 +4,7 @@
 
 - How do we avoid being overwhelmed?
 - How do we make sense of the data?
-- How do we harness this data in decision-making process?
+- How do we harness this data in the decision-making process?
 
 ###### Objective
 
@@ -23,26 +23,21 @@ 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.
+However, by 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
+    - Very common, easy to understand, visually appealing
+    - Can make comparisons harder with many segments
 - Bar chart
-  - Makes comparisons between bars easier
+    - Makes comparisons between bars easier
 - Calendar View
-  - https://observablehq.com/@d3/calendar
-  - Can spot long-term trends - e.g. seasonal, annual trends
+    - 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

@@ -14,23 +14,23 @@
 
 **Record** information
 
-- Blueprints, photographs, seimographs
+- Blueprints, photographs, seismographs
 
 **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
+    - Think of Florence Nightingale using a graph to show that 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
+    - Think of the London tube map: before it was geographically accurate, now it is only topologically accurate
 
-Analysis data to **support reasoning**
+Analyse data to **support reasoning**
 
 - Find patterns
-  - Think the London Cholera map, how John Snow found out where the infection was coming from
+    - Think of 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
+    - Imagine doing a sum like $34\times 52$ mentally versus with a pen and paper
+    - Visualising the sum (column multiplication) can expand your memory
 - Develop and assess hypotheses
 
 #### Different Stages of Visualisation
@@ -38,11 +38,11 @@ Analysis data to **support reasoning**
 ![1645033524.png](img/1645033524.png)
 
 - Data transformation
-  - Create a structural model, schema, mapping raw data into data tables
+    - Create a structural model, schema, mapping raw data into data tables
 - Visual Mapping
-  - Create a visual spatial model, transforming data tables into visual structures
+    - 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.
+    - Create views of the Visual Structures by specifying graphical parameters such as position, scaling and clipping.
 
 ![1645033689.png](img/1645033689.png)
 
@@ -61,12 +61,12 @@ Analysis data to **support reasoning**
 ###### 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
+    - Work out mean, standard deviation etc
 
 ###### Represent
 
-- Choose a visual model 
-  - Bar chart, graph, pie chart etc
+- Choose a visual model
+    - Bar chart, graph, pie chart etc
 
 ###### Refine
 
@@ -78,6 +78,5 @@ Analysis data to **support reasoning**
 
 ##### Interaction is Vital for Exploration
 
-- Engage in a dialog with your data
+- Engage in a dialogue with your data
 - Employ interaction in a more fundamental manner to strengthen the power of visualisation
-