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AI and Machine Learning > Data Visualisation > Flashcards

Data Visualisation Flashcards

1
Q

Tabular Data

A

Data on a table

e.g. all movies

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2
Q

Cross Tabular

A

Linked Categorised tables
e.g. SQL, directors

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3
Q

Perceiving

A

Colours, the natural impact of a graph.

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4
Q

Interpreting

A

Is there enough information to understand what the graph is doing?

E.g. axis labels, scale, title

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5
Q

Comprehending

A

What the data means

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6
Q

TNOIR

A

Qualitative
.Textual
.Nominal
.Ordinal

Quantitative
.Interval
.Ratio

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7
Q

Textual

A

Describing in words e.g. written reviews

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8
Q

Nominal

A

Categorisation without order e.g. the books are in: English, French, German etc.

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9
Q

Ordinal

A

Categorisation with order e.g. the coffee was: Good, Medium, Bad

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10
Q

Interval

A

Scale with an arbitrary zero value e.g. temperature, shoe size, dates

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11
Q

Ratio

A

Scale with a non-arbitrary zero value e.g. distance, age, speed etc.

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12
Q

Temporal

A

To do with time,

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13
Q

Cleaning data

A

Getting rid of nulls, rows with nothing in them, typos, incorrect data types

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14
Q

Marks

A

visual elements such as bars or points

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15
Q

Attributes

A

characteristics of marks
e.g. size, colour

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16
Q

Colour

A

Differentiating data using colour

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17
Q

Saturation

A

Differentiating data using saturation

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18
Q

Scale

A

Differentiating data using scale

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19
Q

Quantising

A

Converting continuous data to discrete.
E.g. 1-100 age scale becomes 10 groups 1-10,11-20,21-30 etc.

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20
Q

Distortion

A

Making data less easy to read in order to distort its meaning. E.g. a 3D pie chart where the slice closets to the observer looks bigger than it is.

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21
Q

Integrity

A

Source, access, change transparency

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22
Q

Linking Data

A

Clicking on a piece of data and moving to another graph

23
Q

Constraints around data visualisation

A

Software, training, budget, staff, time, data, hardware, format

24
Q

Purpose map

A

helps with design of visualisation, target audience
decides whether visualisation is: Exhibatory, exploratory, or explanatory

25
Q

Accessibility

A

What data can be interpreted from a graph, who has access to that data

26
Q

Functional harmony

A

Data that is related should have similar formatting, font, aesthetic etc.

27
Q

MetaData

A

Data about the data, provides context / enhances understanding

28
Q

Continuous

A

data that can be an infinite number of values within a certain range
e.g. temperature, time, weight

29
Q

Discrete

A

data that has distinct values, cant be a fraction
e.g. shoe size,

30
Q

Integer

A

whole number with not decimal parts

31
Q

Boolean

A

True/False

32
Q

Float

A

both integer and fractional

33
Q

String

A

alphanumeric characters

34
Q

Date/Time

A

dates and time

35
Q

Smallint

A

stores integers within a specific range (16 bit),
For when memory is a limitation

36
Q

Complex

A

Complexity is the amount of information displayed in a graph

37
Q

Complicated

A

Complication is the result of too much complexity, it is confusion caused by unclear information

38
Q

Functional harmony

A

ensuring that visual elements work together cohesively and effectively convey information

39
Q

Constraints

A

limitations or restrictions that influence the design of a visualisation

40
Q

Histogram

A

a Barchart showing continuous data (no gaps between bars)

41
Q

Clustered Bar

A

a bar chart that shows more than one set of data within each category (two bars, per x-axis label)

42
Q

ordered bar

A

bars are ordered by size

43
Q

unordered bar

A

bars aren’t ordered, or are ordered by an independent factor e.g. months

44
Q

scatter and line difference

A

(see picture)

45
Q

polar chart

A

(see picture)

46
Q

waffle

A

(see picture)
House of Commons

47
Q

bubble chart

A

(see picture)

48
Q

area graph

A

(see picture)

49
Q

word cloud

A

(see picture)

50
Q

tree map

A

(see picture), like hard drive visualiser (winDirStat)

51
Q

radar chart

A

(see picture) like a polar chart but shows relationships

51
Q

web chart

A

(see picture)

52
Q

box plot

A

(see picture)
median, maximum, minimum, outliers

AI and Machine Learning (6 decks)

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