Data Visualisation
collect
process
store
describe
model
Recap: What is Data Science?
Describing data is an act of compressing the information to focus on
One way is to compute statistics
The other way is to visualise the data
Data visualisation - What
visual encoding of data
Data visualisation - What
visual encoding of data
This encoding can be identified by us,
but can be decoded only by machines
What we are interested in are representations that are decoded by us
Data visualisation - What
visual elements for encoding data
We make perceptual errors in decoding these elements
Data visualisation - What
Data visualisation - What
A
B
Our goal is to reduce perceptual error
Which is longer A or B?
Data visualisation - What
A
B
Our goal is to reduce perceptual error
Which is longer A or B?
Data visualisation - Why
discover insights from the data
RangeIndex: 53940 entries, 0 to 53939
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 carat 53940 non-null float64
1 cut 53940 non-null object
2 color 53940 non-null object
3 clarity 53940 non-null object
4 depth 53940 non-null float64
5 table 53940 non-null float64
6 price 53940 non-null int64
7 x 53940 non-null float64
8 y 53940 non-null float64
9 z 53940 non-null float64
dtypes: float64(6), int64(1), object(3)
memory usage: 4.1+ MB diamonds dataset
Data visualisation - Why
discover insights from the data
communicate insights effectively
Data visualisation - Why
discover insights from the data
communicate insights effectively
Survey of how Americans spend their day
by NYTimes
Animated gapminder plot by Hans Rosling
Pitfalls in Data Visualisation
It is not that important
Pitfalls in Data Visualisation
It is about packing information in an image
Pitfalls in Data Visualisation
It is about drawing cool images
Pitfalls in Data Visualisation
It is about drawing cool images
Pitfalls in Data Visualisation
Aesthetic features like colour don't matter
