In today’s world, we are often flooded with information and encounter more data than we can make sense of. Thus, the need to be able to present data which can be processed quickly is more acute now than ever. Humans are predisposed to recognise patterns, colours, shapes, length etc They can process complex information that has been presented as a visual more quickly than raw data organised in a table. MIT neuroscientists have reported that a human brain can process an image within 13 milliseconds, that is in the blink of an eye.
We can visualise various types of data eg. numerical, spatial, temporal, textual, etc using appropriate tools to convey and deliver our message.
There are two goals when presenting data: convey your story and establish credibility - Edward Tufte
Broadly speaking, a data visualisation may serve one of these 2 purposes:
a) to explain something, or
b) to allow exploration of the data
In the academic context, visualisations can take these forms:
Fry (2008) suggested the following 7 steps in visualising data:
1. Acquire: Obtain the data
2. Parse: Provide a structure
3. Filter: Remove all but the data of interest
4. Mine: Apply mathematical / statistical methods or methods from data mining
5. Represent: Choose a visual model
6. Refine: Further improvements or refinements to the representation
7. Interact: Add methods that allow a user to explore / manipulate the data
While these 7 stages are presented in a linear manner, the process, to a large extent, is iterative.
There are multiple tools that support the visualisation of different types of data:
Open Refine and QGIS are open-source tools while ArcGIS & Tableau offer freemium models.
The Seven C's of Data Analysis proposed by McDaniel & McDaniel (2012):
1. Choose your questions
2. Collect your data
3. Check out your data
4. Clean up your data
5. Chart your analysis
6. Customise your analysis
7. Communicate your results
The following are accessible from the 7 workstations in StarGate:
R and RStudio
Books and Blog Posts
Ang, S. (2019, March 18). From the text rose a cloud of words. Posted in NTU Library Blog.
Berinato, S. (2016). Good charts: the HBR guide to making smarter, more persuasive data visualizations. Boston, MA: Harvard Business Review Press.
Fry, B. (2008). Visualizing data. Sebastopol, CA:O'Reilly Media Inc..
Knafic, C. N. (2015). Storytelling with data: a data visualization guide for business professionals. Hoboken, NJ: Wiley.
McDaniel, E. & McDaniel, S. (2012). The accidental analyst: Show your data who's boss. Seattle, WA: Freakalytics.