INFORMATION
VISUALISATION

For our project, we were assigned to use several visualization tools such as Tableau, Python, D3, and Gephi to create different visualizations based on a dataset of our choosing. The same dataset was used for Tableau, Python (data visualization), and D3 to illustrate the differences in visualization methods and to understand the challenges of using GUI and programming for visualization.
Upon examining the visualizations, we found that Tableau offers a wide range of visualization options that can be created simply by placing rows and columns from our data in the GUI. Python and D3, however, require more programming skills to create visualizations. For instance, creating a basic bar graph in Python or D3 requires checking for NULL values and removing them, whereas Tableau can do this with the click of a button.
Comparing Python and D3, we concluded that D3 is more difficult to work with as it requires a solid understanding of objects and object selection in JavaScript. However, D3 is better at creating more interactive visualizations that are easier to comprehend.
Lastly, we found that Gephi is an excellent tool for network visualization, but it can be challenging to collect data in the proper format. For example, if data is collected from a social media website API, it must be processed in Python to convert it to a .gexf format, which involves converting the data structure to nodes and edges format.