QUESTIONS OF THE DATA VISUAL
Data has been represented using a bar graph, this is also a way of graphically representing data. the graph is clear and precise showing how complex the data set can be simplified. The graph contains data from Americans who were asked to respond to their feeling about trump administration. The survey which was undertaken through an online platform in a bid to investigate whether the Trump administration was delivered according to their campaign promises. The country is preparing itself for another election and this data visualization is very important to show how satisfied Americans are. There were more than 400 respondents and they were to choose among five response actions that are if they felt satisfied, Unsatisfied, Neutral, Very satisfied and Very unsatisfied. I chose a graph to represent data since it will help my audience to understand the data quickly. The graph Cleary shows that most of the respondents were either dissatisfied while others thought it was doing just okay and they were neither satisfied nor dissatisfied.
The graph also shows the relationships between the Trump administration and the Americans. The viewer is also able to compare the different bars at a glimpse. The audience that I target is the trump administration since this data will be useful in making them do some changes to what has not been done as well as improving their governance. I composed my visual through this tactic since it could be easier to show the change over time. Also, the bars are clearer and easier to read Bar graphs are widely used and most audiences can read the graph more easily rather than raw data. the graph can grasp large quantities of data without complicating how it appears. If there are changes with the Trump administration the graph will show the change over time. This is a very important aspect of data visuals. The weekly readings have helped me in understanding how data visualization can simplify data and with the graph above I have been able to understand several data analytics skills.
Reference
Kirk, A. (2016). Data visualization: A handbook for data-driven design. Sage.