Visualising Data

Question 1

Data Visualisation

Answer 1

The graphic representation and presentation of data

Question 2

The McCandless Method

Answer 2

1. Information (data)
2. story (concept)
3. goal (function)
4. visual form (metaphor)

Approaching the method to see parts of the graphic where there is incomplete overlap between all four elements. Visual form without a goal, story, or data could be a sketch or even art.

Question 3

Kaiser Fung’s Junk Chart Triefecta Checkup

Answer 3

1. What is the practical question?
2. What does the data say?
3. What does the visual say?

Captures how to organise the thinking behind the critique pieces

Question 4

The pre-attentive attributes

Answer 4

are elements of a data visualisation that people recognise automatically without conscious effort. The essential, basic building blocks that make visuals understandable are called marks can channels

Question 5

Marks

Answer 5

Basic visual object like points, lines, and shapes. Every mark can be broken down into 4 qualities
1. Position - Where specific marks is in space to a scale or to other marks.
2.Size. How big,small, or tall a mark is
3.Shape - Whether a specific object is given a shape that communicates something about it.
4. Colour

Question 6

Channels

Answer 6

Visual aspects or variables that represent characteristics of the data. Channels are marks that have been used to visualise data. Channels vary in terms of how effective they are based on 3 points

1. Accuracy - Are channels helpful in accurately estimating the represented value. Colour is good at differentiating apples and oranges but not 5 from 5.5
2. Popout—How easy it is to distinguish certain values from others, such as drawing attention to a specific part of a visual.
3. Grouping - How good is a channel at communicating groups that exist in the data - proximity, similarity, enclosure, connectedness, and continuity of the channel

Question 7

Histogram

Answer 7

A chart that shows how often data values fall into certain ranges

Question 8

Correlation charts

Answer 8

show relationships among data

Question 9

Causation

Answer 9

Occurs when an action directly leads to an outcome

Question 10

Correlation

Answer 10

In statistics is the measure of the degree to which two variables move in relationship to each other. An example of correlation is idea that “as the temputure goes up, ice cream sales go up”. It means they have a pattern or some relationship towards each other, butit does not suggest causation.

Question 11

Static Visualisations

Answer 11

Do not change over time unless they’re edited

Question 12

Dynamic visualisations

Answer 12

Interactive or change over time

Question 13

Tableau

Answer 13

A business intelligence and analytics platform that helps people see, understand, and make decisions with data

Question 14

Line Chart

Answer 14

Use to track changes over short and long periods of time. When smaller changes exist, line charts are better to use than bar charts.

Question 15

Column chart

Answer 15

Use size to contrast and compare two or more values, using height or lengths to represent the specific values.

Question 16

Heatmap

Answer 16

Mainly used to show relationships between two variables and use a system of colour-coding to reprsent different values.

Question 17

Pie chart

Answer 17

Is a circular graph that is divided into segments representing proportions corresponding to the quantity it represented, especially when dealing with parts of a whole.

Question 18

Scatterplot

Answer 18

Show relationship between different variables. Typically used for two variables for a set of data, although variables can be displayed.

Question 19

Distribution graph

Answer 19

Displays the spread of various outcomes in a dataset

Question 20

Change

Answer 20

Trend or instance of observations that become different over time. A great way to measure change in data is through a line or column chart

Question 21

Clustering

Answer 21

Collection of data points with similar or different values. This is best represented through a distribution chart.

Question 22

Relativity

Answer 22

These are observations considered in relation or in proportion to something else. You have probably seen examples of relativity data in a pie chart.

Question 23

Ranking

Answer 23

This is a position in a scale of achievement or status. Data that requires ranking is best represented by a column chart.

Question 24

Correlation

Answer 24

This shows a mutual relationship or connection between two or more things. A scatterplot is an excellent way to represent this type of data pattern.

Question 25

Decision Tree

Answer 25

tool that allows data analyst to make decisions based on key questions that you can ask yourself.

Question 26

The elements of art in data analytics

Answer 26

Line Shape Colour Space Movement (Sense or flow or action in visualisation)

Question 27

Colour

Answer 27

Hue (Red blue green) Intensity (bright or dull) Value (How much light is being reflected) All of these aspects are within visualisations. You can have a shade of grey, that is dull, but with different tints to display data.

Question 28

9 Principles of Design

Answer 28

Balance Emphasis Movement Pattern Repetition Proportion Rhythm Variety Unity

Question 29

6 key design principles when you are creating your viz?

Answer 29

Balance Emphasis Movement Pattern Repetition Proportion

Question 30

3 key design principle when you are checking you viz

Answer 30

Rhythm Variety Unity

Question 31

Data composition

Answer 31

Combining the individual parts in a viz and displaying them together as a whole

Question 32

Elements for effective visuals

Answer 32

Clear meaning Sophisticated use of contrast - separate most important data from the rest Refine execution - deep attention to detail using elements of art.

Question 33

Design Thinking

Answer 33

A process used to solve complex problems in a user-centric way

Question 34

Five phases of the design process

Answer 34

Empathise - consider how your audience will visualise the data (too birght/dramatic for seriousness of data ect) Define - define audience needs, problems and insights, goes hand in hand with emphasise phase. Ideate - Start to generate data viz ideas from define and emphasise phase - drafts example. Creating as many examples as possible. Prototype - Putting your chart/dashboard together Test- putting your chart/dashboards together.

Question 35

Headline

Answer 35

A line of words printed in large letters at the top of the visualisation to communicate what data is being presented

Question 36

Subtitle

Answer 36

Supports the headline by adding more context and description

Question 37

Legend

Answer 37

Identifies the meaning of various element in a data visualisation

Question 38

Alternative text

Answer 38

Alternative text provides a textual alternative to non-text content