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Digital Tools and Methods

List of tools & services to support data visualisation, data cleaning & analysis, and digital publishing

Introduction to Data Visualisation

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

Data Visualisation

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:

  • Scientific visualisations
  • Information visualisation / infographics

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: 

  • Geo-spatial: ArcGIS, QGIS, Tableau
  • Data visualisation: Gephi, Excel, MATLAB, Tableau
  • Data cleaning: Open Refine, Excel, Tableau
  • Text visualisation: Word Clouds, network graphs

Open Refine and QGIS are open-source tools while ArcGIS & Tableau offer freemium models.



Data Analytics

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

Data Analysis & Visualisation Software

The following are accessible from the 7 workstations in StarGate:





R and RStudio


Additional Resources

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.