PRACTICES OF DATA VISUALIZATION

Running head: PRACTICES OF DATA VISUALIZATION
PRACTICES OF DATA VISUALIZATION
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PRACTICES OF DATA VISUALIZATION1
Table of Contents
1. Introduction............................................................................................................................2
2. Discussion..............................................................................................................................3
2.1. Good practices of Data Visualization.............................................................................8
2.2. Bad practices of Data Visualization..............................................................................16
3. Conclusion............................................................................................................................25
4. References............................................................................................................................26

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1. Introduction
Data visualization is the universal term which expresses any effort that help the
individuals to recognise the significance of the data by insertion it in the visual context. The
trends, correlations also patterns which may go unobserved in the data that is text based can
expose also identify easily with this software of data visualization (Murray 2017). It mentions
to the methods which is used to interconnect visions from the data via visual representation.
The core objective of this is to filter the huge sets of data into the visual graphics to permit
for the informal understanding of the complex relationships within data (Thorvaldsdóttir,
Robinson and Mesirov 2013). Sometime it is used interchangeably with the terms like
statistical graphics, information visualization and information graphics. It is the stage of the
process of data science that developed by the Joe Blitzstein that is the framework to approach
the tasks of the data science.
After collecting then processing and lastly modelling the data, this relationships need
to visualize so that the conclusion can make from this (Dzemyda, Kurasova and Zilinskas
2013). Also it is the element of wider discipline of the DPA which is Data Presentation
Architecture that seeks for recognizing, locating, manipulating, formatting also presenting the
data in very efficient way (Otten, Cheng and Drewnowski 2015). The data visualization is
very much important now a days because there are a huge quantity of data is generated every
day and for any person it is impossible to handle those data line by line also see the individual
patterns and create observations (Dzemyda, Kurasova and Zilinskas 2013). So this can be
managed by the Data proliferation which is the part of process of Data science and it contains
the Data Visualization.
Today tools of data visualization are above of all standard charts also the graphs in the
Microsoft Excel spreadsheets which display the data more sophisticated way like

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infographics, geographic maps, heat maps, dials and the gauges, detailed bar, pie, fever charts
and sparklines (Ward, Grinstein and Keim 201. This data visualization become the major part
for the modern business intelligence (BI). The tools of the data visualization have been more
significant in democratizing data also analytics and creating data driven perceptions
accessible to the staffs all the way through any organization.
2. Discussion
Data visualization is an act of gathering the information or data also placing this into
visual context like graph or map. It make small also big data easiest for the brain of human to
understand also visualization makes this easiest for detecting the trends, outliers and patterns
in the groups of this data. There are many types of visuals present which are pie chart, table,
bar graph, radial trees, heat maps, bullet graphs, infographics, bubble clouds and many more.
Some of the example of the above mentioned visualization are as given below:
Figure 1: Example of Table visualization
(Source: Burrough 2015)

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Figure 2: Example of Bar Graph Visualization
(Source: Silge and Robinson 2016)
Figure 3: Example of Bubble Cloud Visualization

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(Source: Murray 2017)
Figure 4: Example of Pie Chart Visualization
(Source: Dzemyda, Kurasova and Zilinskas 2013)
Figure 5: Example of Heat Maps Visualization
(Source: Chen, Guo and Wang 2015)